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Green area index from an unmanned aerial system over wheat and rapeseed crops

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... They can be operated in a near real-time and dynamic manner and are relatively low-cost [4]. Significant progress has been achieved to extract traits such as crop height, the cover fraction, green area index (GAI), or chlorophyll and nitrogen contents from different optical sensors [5][6][7][8][9]. Among these optical sensors, multispectral cameras working in the visible (400-700 nm) and near-infrared (700-1000 nm) spectral domain are well suited for vegetation monitoring [10,11]. ...
... Among these optical sensors, multispectral cameras working in the visible (400-700 nm) and near-infrared (700-1000 nm) spectral domain are well suited for vegetation monitoring [10,11]. The images can be either used to calculate vegetation indices directly [5] or to estimate canopy structural traits such as GAI [8,12] and biochemical traits such as chlorophyll content [13]. Despite remarkable research and applications, there are still some bottlenecks that limit the efficiency and accuracy of multispectral cameras onboard UAVs. ...
... In the first step, some unusable images (e.g., images taken during take-off and landing and blurred images) were firstly discarded. The vignetting effect of cameras was then corrected as described in [8], and the images from the several cameras of the multispectral camera shot with the same trigger were coregistered based on image similarity [25] with accuracy finer than one pixel. ...
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Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the throughput of data acquisition while not degrading the ground sample distance (GSD) is, therefore, a critical issue to be solved. We propose here a new image acquisition configuration based on the combination of two focal length ( f ) optics: an optics with f = 4.2 mm is added to the standard f = 8 mm (SS: single swath) of the multispectral camera (DS: double swath, double of the standard one). Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude. The DS flight plan was designed to get 80% overlap with the 4.2 mm optics, while the SS one was designed to get 80% overlap with the 8 mm optics. As a result, the time required to cover the same area is halved for the DS as compared to the SS. The georeferencing accuracy was improved for the DS configuration, particularly for the Z dimension due to the larger view angles available with the small focal length optics. Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one. However, for both the DS and SS configurations, degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.
... Vegetation indices based on UAV imagery have shown the same capability to quantify crop responses as ground-based sensors. However, it is necessary to consider the angular variation in reflectance [29] and ambient light fluctuations [30]. ...
... The variation throughout the day in the vegetation indices calculated from UAVs has been reported in previous works. Rasmussen et al. (2016) [30] and Verger et al. (2014) [29] found that vegetation indices based on UAV imagery had the same capability to quantify crop responses as groundbased sensors. However, it is necessary to consider the angular variation in reflectance [29] and ambient light fluctuations [30]. ...
... The variation throughout the day in the vegetation indices calculated from UAVs has been reported in previous works. Rasmussen et al. (2016) [30] and Verger et al. (2014) [29] found that vegetation indices based on UAV imagery had the same capability to quantify crop responses as groundbased sensors. However, it is necessary to consider the angular variation in reflectance [29] and ambient light fluctuations [30]. ...
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Citation: Souza, R.d.; Buchhart, C.; Heil, K.; Plass, J.; Padilla, F.M.; Schmidhalter, U. Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV. Remote Sens. 2021, 13, 1691. https://doi.
... Recent advances in the development of unmanned aerial vehicle (UAV)-based proximal sensing have provided a potentially more useful alternative that could meet the requirement of high spatial and temporal resolution for field phenotyping (Yang et al., 2020). Studies have demonstrated the capability of UAV proximal sensing in the estimation of LAI (Duan et al., 2014), flower number (Wan et al., 2018), ear number (Fernandez-Gallego et al., 2020), green area index (Verger et al., 2014), water stress (Zarco-Tejada et al., 2012), biomass (Cen et al., 2019), yield (Maimaitijiang et al., 2020;Wan et al., 2020), chlorophyll, and nitrogen status (Wang et al., 2018;Jay et al., 2019). However, there is a trade-off between the signalto-noise ratio and the spectral/spatial resolutions of images acquired from UAV-based proximal sensing, resulting in UAV data interpretation being a major scientific challenge (Deng et al., 2018a, b;Hu et al., 2019;Wang et al., 2019). ...
... In addition, the empirical relationships between spectral data and FVC were affected by sensor conditions and environmental variabilities (Lu et al., 2003). Compared with the empirical methods, mechanistic methods offer the advantage of including a much wider range of situations (Verger et al., 2014;Weiss et al., 2020), which have aroused much interest in the retrieval of crop growth parameters based on the mechanistic linkages between the growth parameters and optical properties. One of the most popular mechanistic methods, the PROSAIL model, which was developed by coupling the leaf PROSPECT model (Jacquemoud and Baret, 1990) with the canopy SAIL model (Verhoef, 1984), provides a great advantage for estimating crop physiological and structural parameters with high accuracy and robustness (Jacquemoud et al., 2009;Verrelst et al., 2019;Weiss et al., 2020). ...
... These findings were consistent with the actual observations of rice, wheat, oilseed rape, and cotton presented in Supplementary Fig. S9. In addition, several previous studies displayed the actual LAD in wheat (Danner et al., 2019), rice (Zhang et al., 2017), oilseed rape (Verger et al., 2014), and cotton (Shah et al., 2017). Leaf normals in rice and wheat were oriented in all directions with equal probability, which was consistent with the spherical distribution. ...
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Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is a challenging task, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling PROSAIL with gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined with rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and the high accuracy of FVC retrieval was obtained with rRMSE of 12%, 6%, and 6%, respectively. The findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which would be a promising tool for precision crop breeding.
... Timely monitoring of agricultural production and accurate prediction of crop yield are crucial due to the severe economic and social consequences of food shortages [1,2]. Especially, agricultural products have to be increased by 70% by 2050, when the world population is expected to reach 9 billion people [3]. ...
... There are three types of crop yield estimation methods based on remote sensing data; (1) common empirical models based on vegetation indices (VIs) [9][10][11][12][13], (2) crop yield estimation by mechanistic models combined with remote sensing data [14][15][16][17][18], and (3) semi-empirical production estimation models based on Gross Primary Production (GPP) or Net primary production (NPP) [19,20]. Among them, empirical models based on VIs are the most widely used method for crop yield estimation due to their simplicity and ease of use [21,22]. ...
... Carl et al. [32] extracted the proportion of flower area and estimated the florets amount of acacia using unmanned aerial vehicle (UAVs) images. (2) The effects of flowering information on estimation of such vegetation parameters as biomass, coverage and LAI. Shen et al. [33] found that it was difficult to estimate biomass using NDVI and EVI that contain flowering information, but they could estimate biomass more accurately after the removal of flowering information. ...
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Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.
... Réflectance de la canopée Introduction générale 30 un drone (Jay et al., 2018 ;Verger et al., 2014). L'utilisation des satellites pour l'estimation du GLAI et la prédiction du rendement s'est fortement développée à partir des années 1970. ...
... Les images multispectrales brutes sont d'abord co-registrées d'après la méthode proposée par Rabatel et Labbé (2016). Les images sont ensuite corrigées du vignetage et calibrées grâce à la surface de référence selon l'équation 6 (Verger et al., 2014). Baret (1986), España (1997), et afin de simuler la cinétique du GLAI au cours du cycle, à partir d'un nombre limité de paramètres facilement mesurables au champ : la densité de plantes ( ), le nombre final de feuilles ( ), le phyllochrone ( ), la surface de la plus grosse feuille ( ), et un paramètre de longévité des feuilles ( ) (Figure 24). ...
... In this study, we proposed an innovative way of developing transfer functions, consisting in using spectral predictors concurrently with additional known variables { , , } to predict GLAI dynamics of a large panel. The resulting transfer functions provided good accuracy compared to other studies where GLAI was retrieved from remote sensing observationsKang et al., 2016;Verger et al., 2014;Verrelst et al., 2015;Walthall et al., 2004), especiallygiven the small size of the microplots, and the diversity of the genotypes characterized. However, possible residual genotypic effects related to differences in leaf orientation or aggregation may be present because a unique transfer function was used for all genotypes on each date. ...
Thesis
D’ici la fin du siècle, les prévisions climatiques prévoient une diminution de la quantité et de la régularité des pluies s’accompagnant d’une augmentation du risque de sècheresse en Europe et dans de nombreuses régions du monde. La création de nouvelles variétés de maïs plus tolérantes au stress hydrique est un levier indispensable pour faire face à ces contraintes futures. L’objectif principal de cette thèse est d’approfondir les connaissances des déterminismes génétiques de la tolérance à la sècheresse chez le maïs. Pour ce faire, il est proposé de disséquer ce caractère complexe en caractères physiologiques sous-jacents dont le déterminisme génétique est a priori plus simple. L’évolution de l’indice foliaire vert (GLAI : Green Leaf Area Index) au cours du cycle de la plante, par son rôle majeur dans l’interception lumineuse, la transpiration et les échanges de CO2, est un caractère secondaire prometteur pour identifier les bases génétiques de la tolérance à la sècheresse et en améliorer la compréhension. Au cours de cette thèse, nous avons développé une méthode de phénotypage haut débit permettant d’estimer la cinétique du GLAI au champ. Cette méthode combine la caractérisation multispectrale par drone et l’utilisation d’un modèle physiologique simple de GLAI. Elle permet d’estimer la cinétique du GLAI de manière continue sur l’ensemble du cycle de la plante avec une bonne précision, tout en divisant par vingt le temps nécessaire au phénotypage. Nous avons utilisé cette méthode lors de deux essais en conditions optimales et deux essais en conditions de stress hydrique pour mesurer l’évolution du GLAI au sein d’un panel de 324 lignées issues d’une population MAGIC (Multi-parent Advanced Generation Inter-Cross). Les cinétiques estimées présentent une forte héritabilité et expliquent une part significative du rendement en conditions optimales et stressées. Afin d’identifier les bases génétiques de la cinétique du GLAI, trois approches de génétique d’association longitudinales ont été comparées : une approche univariée en deux étapes, une approche multivariée en deux étapes et une approche de régression aléatoire en une étape. Ces trois approches, couplées à la forte densité des données de génotypage disponibles (près de 8 millions de marqueurs), ont permis de révéler de nombreux QTL (Quantitative Trait Loci), dont certains colocalisent avec des QTL de rendement. Enfin, nous avons démontré que les QTL de GLAI identifiés lors de cette étude pouvaient expliquer près de 20 % de la variabilité du rendement observée dans un large réseau d’expérimentations sous stress hydrique. Ce travail fournit des méthodes qui permettront une meilleure caractérisation et une meilleure compréhension des déterminismes génétiques de la cinétique du GLAI, un caractère jusqu’ici inaccessible pour les populations de taille importante. Ce caractère présente toutes les caractéristiques requises pour améliorer l’efficacité des programmes de sélection en conditions de stress hydrique.
... The applicability of PROSAIL to UAV data has been explored in various studies for various crops. The most widely retrieved variables are green fraction (GF), LAI, LCC and canopy chlorophyll content (CCC) [34][35][36][37][38][39]. In the majority of these studies, UAVs were either flown at high altitudes to produce coarse-resolution imagery that mimics airborne or satellite data [34,35,38], or the resolution of the final orthomosaic was artificially reduced [34]. ...
... The most widely retrieved variables are green fraction (GF), LAI, LCC and canopy chlorophyll content (CCC) [34][35][36][37][38][39]. In the majority of these studies, UAVs were either flown at high altitudes to produce coarse-resolution imagery that mimics airborne or satellite data [34,35,38], or the resolution of the final orthomosaic was artificially reduced [34]. This reduction was done to meet the assumption of a turbid medium model such as PROSAIL. ...
... While these sensors deliver spectrally contiguous data, their applicability in breeding and precision agriculture is currently limited due to their high cost and complex data post-processing. Multispectral sensors, on the other hand, are much cheaper and provide information of important spectral regions, which proved to be sufficient to retrieve crop biophysical variables of comparable quality [35]. A common method of retrieving vegetation variables at plot level is averaging the measured spectra per plot and applying the inversion scheme to it. ...
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Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92g/2, silage maize RMSE = 3.74g/2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70m2/m2, silage RMSE = 0.61m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 g/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.
... Broad theme References 1 Environmental monitoring, crop management, weed management Agüera Vega et al., 2015;de Castro et al., 2018;Gómez-Candón et al., 2014;Y. B. Huang et al., 2013;Khanal et al., 2017;López-Granados, 2011;Manfreda et al., 2018;Pádua et al., 2017;Peña et al., 2013;Pérez-Ortiz et al., 2015;Rasmussen et al., 2013Rasmussen et al., , 2016Torres-Sánchez et al., 2014;Verger et al., 2014;Von Bueren et al., 2015;C. Zhang & Kovacs, 2012) 2 Remote phenotyping, yield estimation, crop surface model, counting of plants (Bendig et al., 2013(Bendig et al., , 2014Geipel et al., 2014;Gnädinger & Schmidhalter, 2017;Haghighattalab et al., 2016;Holman et al., 2016;Jin et al., 2017;W. ...
... Gevaert et al., 2015;Maes & Steppe, 2019). For this reason, AgüeraVega et al. (2015) used a UAV-mounted multispectral sensor system to acquire images of a sunflower crop during the growing season. Similarly,Huang et al. (2009) note that remote sensing based on UAVs could facilitate the measurement of crops and soil from the collected spectral data.Verger et al. (2014) developed and tested a technique for estimating a green area index (GAI) from UAV reflectance measurements in precision agriculture applications, focusing on wheat and rapeseed crops. Therefore, drones provide new possibilities for ...
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Drones, also called Unmanned Aerial Vehicles (UAV), have witnessed a remarkable development in recent decades. In agriculture, they have changed farming practices by offering farmers substantial cost savings, increased operational efficiency, and better profitability. Over the past decades, the topic of agricultural drones has attracted remarkable academic attention. We therefore conduct a comprehensive review based on bibliometrics to summarize and structure existing academic literature and reveal current research trends and hotspots. We apply bibliometric techniques and analyze the literature surrounding agricultural drones to summarize and assess previous research. Our analysis indicates that remote sensing, precision agriculture, deep learning, machine learning, and the Internet of Things are critical topics related to agricultural drones. The co-citation analysis reveals six broad research clusters in the literature. This study is one of the first attempts to summarize drone research in agriculture and suggest future research directions.
... This normalization method highlights the information within the shape of a spectral curve while minimizing the effects of the absolute reflectance value (Wu 2004). Verger et al. (2014) also used normalised reflectance when estimating green-area-index from four-band images taken by unmanned airborne systems. The normalization method eliminated the unstable illumination condition and improved the estimation accuracy. ...
... which reduces the input information and increases the accuracy of the model. In our case, spectral normalisation could remove redundant information, consistent with the work of Wu (2004) and Verger et al. (2014), in which spectral normalisation eliminated the effect of differences in incoming radiation. Secondly, a change in key waveband is notable. ...
Article
This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m²). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R²) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R² values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m². LKC retrieval models had higher R² values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.
... For measurements using in situ nondestructive sensing equipment such as hemispherical photography below the canopy or from airborne/space-borne remote sensing, there are other canopy components apart from leaves, the canopy random architecture and the radiation transfer mechanisms that are included, and LAI does not represent well these elements. This is why the Green Area Index (GAI) [3]- [5] has been proposed, which is related to the photosynthetically active leaves along with the rest of the nonleafy canopy elements, such as stems and ears in the case of cereal crops. ...
... Also, linear/logarithmic models are prone to saturation effects, and they are not sufficient to predict GAI due to the non-linearity of physical relationships [21], [22]. An alternative is to use a deterministic approach based on Radiative Transfer Model (RTM) inversions [5], [23]- [27], but these approaches are too complex to be widely operational [28], [29]. ...
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This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an Unmanned Aircraft System (UAS) and the crop health status was assessed through the Green Area Index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs using machine learning pipelines with several regression algorithms (Multiple Linear Models, Support Vector Machines, Random Forests, and Artificial Neural Networks) along with a feature selection strategy. The Random Forests algorithm was shown to be the best algorithm for GAI prediction with an average relative Root Mean Square Error of 10.86% and a Mean Absolute Error of 0.67. The resulting GAI maps and the regression feature space were classified with Random Forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented.
... In a field experiment, highthroughput phenotyping using unmanned aerial vehicles (UAVs) (Yang et al., 2017) and tractors (White et al., 2012) was used to measure plant growth. Among growth traits, the leaf area index (LAI) is often investigated because it is accessible from high-throughput phenotyping (Verger et al., 2014;Liu et al., 2017) while being sensitive to the environment, directly determining amount of light absorption, and thus affecting biomass production and yield. Until recently, however, these techniques were mainly used for crop management such as estimation of canopy state variables, soil properties and yield (Jin et al., 2018), and their applications to genetic dissection remain limited (Blancon et al., 2019). ...
... and gdal (ver.3.2.2). For data in 2019, a similar procedure was used by Hiphen Inc. 3 The analysis protocol was the same as previous research (Verger et al., 2014;Madec et al., 2017). ...
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With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.
... At present, satellite remote sensing technology is widely used in large-scale agricultural monitoring [6], but it has some shortcomings, such as long revisit periods, coarse resolution and ability to operate in limited meteorological conditions [7,8]. Low-altitude unmanned aerial vehicle (UAV) remote sensing has the advantages of improved spatio-temporal resolution, low operation cost, flexibility and repeatability [9,10]. It can quickly and efficiently acquire centimeterlevel remote sensing images of large areas of farmland and effectively assist agricultural operators in management and decision making [11]. ...
... The band wavelength of multispectral imaging equipment is generally between 400 and 900 nm, mainly including blue, green, red, red edge and near-infrared. Different vegetation indices (VIs) obtained from different bands have been widely used to determine the values of crop biophysical parameters, which can accurately reflect details of crop growth and the response of the crop to stress (e.g., pests, diseases, temperature, soil, water, etc.) [10]. Many researchers have estimated crop physical and chemical parameters, as well as yield, based on VIs extracted from UAV multi-spectral images, which have achieved good results [15][16][17]. ...
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Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.
... Using remote sensing technology to estimate vegetation has been widely developed especially for large-scale and long-term crop monitoring. Compared with traditional methods, UAV remote sensing technology has more advantages, e.g., convenient transportation, high flexibility, short operation time, and low investment, which provide a new method for fast, nondestructive, and high-throughput acquisition of field crop phenotypic information [9][10][11][12]. At the same time, many studies found that crop canopy spectral information was significantly related to crop LAI and CC, and they used spectral reflectance and the vegetation index to build mathematic models for estimating LAI and CC [8,[13][14][15]. ...
... Various algorithms and models have been applied to predict wheat LAI and CC through collecting images with UAV and multispectral sensor technologies [12,35,36]. Gao et al. [36] used the UAV hyperspectral vegetation index to model 103 LAIs of multiple wheat single-growth periods, and the verification R 2 of the model was 0.783. ...
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High-throughput phenotypic identification is a prerequisite for large-scale identification and gene mining of important traits. However, existing work has rarely leveraged high-throughput phenotypic identification into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility and effectiveness of high-throughput phenotypic data obtained from UAV multispectral images in gene mining of important traits is an urgent problem to be solved in wheat. In this paper, 309 lines of the spring wheat Worrakatta × Berkut recombinant inbred line (RIL) were taken as materials. First, we obtained the leaf area index (LAI) including flowering, filling, and mature stages, as well as the flag leaf chlorophyll content (CC) including heading, flowering, and filling stages, from multispectral images under normal irrigation and drought stress, respectively. Then, on the basis of the normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), which were determined by multispectral imagery, the LAI and CC were comprehensively estimated through the classification and regression tree (CART) and cross-validation algorithms. Finally, we identified the QTLs by analyzing the predicted and measured values. The results show that the predicted values of determination coefficient (R2) ranged from 0.79 to 0.93, the root-mean-square error (RMSE) ranged from 0.30 to 1.05, and the relative error (RE) ranged from 0.01 to 0.18. Furthermore, the correlation coefficients of predicted and measured values ranged from 0.93 to 0.94 for CC and from 0.80 to 0.92 for LAI at different wheat growth stages under normal irrigation and drought stress. Additionally, a linkage map of this RIL population was constructed by 11,375 SNPs; eight QTLs were detected for LAI on wheat chromosomes 1BL, 2BL (four QTLs), 3BL, 5BS, and 5DL, and three QTLs were detected for CC on chromosomes 1DS (two QTLs) and 3AL. The closely linked QTLs formed two regions on chromosome 2BL (from 54 to 56 cM and from 96 to 101 cM, respectively) and one region on 1DS (from 26 to 27 cM). Each QTL explained phenotypic variation for LAI from 2.5% to 13.8% and for CC from 2.5% to 5.8%. For LAI, two QTLs were identified at the flowering stage, two QTLs were identified at the filling stage, and three QTLs were identified at the maturity stage, among which QLAI.xjau-5DL-pre was detected at both filling and maturity stages. For CC, two QTLs were detected at the heading stage and one QTL was identified at the flowering stage, among which QCC.xjau-1DS was detected at both stages. Three QTLs (QLAI.xjau-2BL-pre.2, QLAI.xjau-2BL.2, and QLAI.xjau-3BL-pre) for LAI were identified under drought stress conditions. Five QTLs for LAI and two QTLs for CC were detected by imagery-predicted values, while four QTLs for LAI and two QTLs for CC were identified by manual measurement values. Lastly, investigations of these QTLs on the wheat reference genome identified 10 candidate genes associated with LAI and three genes associated with CC, belonging to F-box family proteins, peroxidase, GATA transcription factor, C2H2 zinc finger structural protein, etc., which are involved in the regulation of crop growth and development, signal transduction, and response to drought stress. These findings reveal that UAV sensing technology has relatively high reliability for phenotyping wheat LAI and CC, which can play an important role in crop genetic improvement.
... Simple statistical regression and machine learning regression methods have been widely used to estimate LAI of various vegetation species owing to their ease of application. Some successes have been achieved for LAI estimation of different crops based on simple statistical regression and machine learning regression methods (Boegh et al., 2002;Camacho et al., 2021;Chen et al., 2020;Dong et al., 2019;Kira et al., 2016;Liang et al., 2015;Mananze et al., 2018;Panigrahi and Das, 2020;Sun et al., 2021;Verger et al., 2014;Vina et al., 2011;Zhang et al., 2021aZhang et al., , 2021bZhao et al., 2007). ...
... (2) can a generic model be used to accurately estimate LAI for multiple crops? Additionally, the utility and the differences between simple statistical regression methods, machine learning methods, and the radiative transfer model for LAI estimation of different crops still need to be further investigated (Baret and Guyot, 1991;Camacho et al., 2021;Dong et al., 2019;Duveiller et al., 2011;Kang et al., 2021;Korhonen et al., 2017;Liang et al., 2015;Panigrahi and Das, 2020;Punalekar et al., 2018;Sehgal et al., 2016;Verger et al., 2014;Wang et al., 2018;Yao et al., 2017;Zhang et al., 2021a). In order to answer the above questions, the simple statistical regression, machine learning regression, and radiative transfer model were used to estimate LAI of several crops grew in different years and sites. ...
Article
The leaf area index (LAI) is an important parameter indicating the crop growth status. Many vegetation indices and models have been developed to estimate LAI of different crops. However, the utility of and differences between crop-specific and generic algorithms for LAI estimation covering several crops, dates, and sites need to be compared. The main objectives of this study were to: (1) evaluate the suitability and robustness of different vegetation indices for LAI estimation of different crops; (2) compare the performance of crop-specific and generic algorithms; (3) evaluate the performance of statistical regression, machine learning regression, and the PROSAIL-D with different inversion strategies. Results showed that: (1) the simple ratio index (SR) performed best for cotton and winter wheat, the MERIS terrestrial chlorophyll index (MTCI) performed best for maize; (2) the models trained over specific crop types performed better than those trained over all crop types together; (3) for crop-specific models, artificial neural networks (ANN) performed better than support vector machine regression (SVR), partial least square regression (PLSR), and statistical regression methods, while the PROSAIL-D yielded similar or slightly better performance as compared with ANN method. Furthermore, the look-up table (LUT) strategy performed better than iterative optimization strategy (Shuffled Complex Evolution method developed at the University of Arizona, SCE-UA). Results indicated that there was not an optimal model that could be used to precisely estimate LAI of different crops since the relationship between the specific hyperspectral reflectance and the LAI of different crops were different. In addition, the PROSAIL-D with crop specific input parameters is a potential method for LAI estimation of different crops with hyperspectral reflectance.
... Traits were averaged per plot and spatially adjusted as in the multi-site experiment below. In experiment j, images were taken from a hexacopter Atechsys (http://atechsys.fr/) that carried a six-channel multispectral camera (Hi-Phen modèle V3 AirPhen 6 bands (450-532-568-675-730-850). The leaf area index was calculated as the projected leaf area per unit ground surface area, generated by inverting the radiative transfer model PROSAIL 81 . The fraction of intercepted photosynthetically active radiation was calculated from RGB images. ...
... The fraction of intercepted photosynthetically active radiation was calculated from RGB images. The mean leaf angle was calculated by inverting the radiative transfer model PROSAIL 81 . Other variables calculated from these images are presented in the datafile https://doi.org/ ...
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Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha−1 year−1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits. Phenomic and genomic approaches are required to evaluate the progress of breeding strategies. Here, the authors analyse 65 years of genetic progress in maize, showing that breeders have selected traits with stable effects on yield whereas not for adaptive traits key for climate change adaptation.
... Another relevant advantage is the time taken for each measurement (<6 s) compared to those performed with an infrared gas analyzer (<6 min) to characterize leaf gas exchange (net CO 2 assimilation rate (An), gs, internal CO 2 concentration, and transpiration rate) [43][44][45], facilitating the performance of a more significant number of readings in a day. For example, the maximum photochemical quantum yield (Fv/Fm) of photosystem II (FSII), a parameter related to An [46], has been used to identify spring wheat genotypes that were tolerant to high temperatures [47,48]. To select wild wheat genotypes tolerant to water deficit, along with Fv/Fm, other authors have also suggested reading the minimum and maximum fluorescence under dark conditions (F0 and Fm, respectively) [49]. ...
... For spectral traits, vegetation indices (VI) calculated from multispectral images acquired by UAV are used for evaluating vegetation properties, such as plant structure, biochemistry, and plant physiological and stress status [31,33,34,[40][41][42][43][44][45][46][47][48]. A large number of VIs have been proposed and have been used in ground-based platforms, aircraft, and satellite remote sensing. ...
Article
The development of RGB (red, green, blue) sensors has opened the way for plant phenotyping. This is relevant because plant phenotyping allows us to visualize the product of the interaction between the plant ontogeny, anatomy, physiology, and biochemistry. Better yet, this can be achieved at any stage of plant development, i.e., from seedling to maturity. Here, we describe the use of phenotyping, based on the stay-green trait, of common bean (Phaseolus vulgaris L.) plant, as a model, stressed by water deficit, to elucidate the result of that interaction. Description is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software. These images allow us to obtain a data group related to the color parameters that quantify the changes and alterations in each plant growth and development.
... Another relevant advantage is the time taken for each measurement (<6 s) compared to those performed with an infrared gas analyzer (<6 min) to characterize leaf gas exchange (net CO 2 assimilation rate (An), gs, internal CO 2 concentration, and transpiration rate) [43][44][45], facilitating the performance of a more significant number of readings in a day. For example, the maximum photochemical quantum yield (Fv/Fm) of photosystem II (FSII), a parameter related to An [46], has been used to identify spring wheat genotypes that were tolerant to high temperatures [47,48]. To select wild wheat genotypes tolerant to water deficit, along with Fv/Fm, other authors have also suggested reading the minimum and maximum fluorescence under dark conditions (F0 and Fm, respectively) [49]. ...
... For spectral traits, vegetation indices (VI) calculated from multispectral images acquired by UAV are used for evaluating vegetation properties, such as plant structure, biochemistry, and plant physiological and stress status [31,33,34,[40][41][42][43][44][45][46][47][48]. A large number of VIs have been proposed and have been used in ground-based platforms, aircraft, and satellite remote sensing. ...
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High-throughput phenotyping platforms for growth chamber and greenhouse-grown plants enable nondestructive, automated measurements of plant traits including shape, aboveground architecture, length, and biomass over time. However, to establish these platforms, many of these methods require expensive equipment or phenotyping expertise. Here we present a relatively inexpensive and simple phenotyping method for imaging hundreds of small- to medium-sized growth chamber or greenhouse-grown plants with a digital camera. Using this method, we image hundreds of tomato plants in 1 day.
... Another relevant advantage is the time taken for each measurement (<6 s) compared to those performed with an infrared gas analyzer (<6 min) to characterize leaf gas exchange (net CO 2 assimilation rate (An), gs, internal CO 2 concentration, and transpiration rate) [43][44][45], facilitating the performance of a more significant number of readings in a day. For example, the maximum photochemical quantum yield (Fv/Fm) of photosystem II (FSII), a parameter related to An [46], has been used to identify spring wheat genotypes that were tolerant to high temperatures [47,48]. To select wild wheat genotypes tolerant to water deficit, along with Fv/Fm, other authors have also suggested reading the minimum and maximum fluorescence under dark conditions (F0 and Fm, respectively) [49]. ...
... For spectral traits, vegetation indices (VI) calculated from multispectral images acquired by UAV are used for evaluating vegetation properties, such as plant structure, biochemistry, and plant physiological and stress status [31,33,34,[40][41][42][43][44][45][46][47][48]. A large number of VIs have been proposed and have been used in ground-based platforms, aircraft, and satellite remote sensing. ...
Article
High-throughput phenotyping enables the temporal detection of subtle changes in plant plasticity and adaptation to different conditions, such as nitrogen deficiency, in an accurate, nondestructive, and unbiased way. Here, we describe a protocol to assess the contribution of nitrogen addition or deprival using an image-based system to analyze plant phenotype. Thousands of images can be captured throughout the life cycle of Arabidopsis, and those images can be used to quantify parameters such as plant growth (area, caliper length, diameter, etc.), in planta chlorophyll fluorescence, and in planta relative water content.
... Another relevant advantage is the time taken for each measurement (<6 s) compared to those performed with an infrared gas analyzer (<6 min) to characterize leaf gas exchange (net CO 2 assimilation rate (An), gs, internal CO 2 concentration, and transpiration rate) [43][44][45], facilitating the performance of a more significant number of readings in a day. For example, the maximum photochemical quantum yield (Fv/Fm) of photosystem II (FSII), a parameter related to An [46], has been used to identify spring wheat genotypes that were tolerant to high temperatures [47,48]. To select wild wheat genotypes tolerant to water deficit, along with Fv/Fm, other authors have also suggested reading the minimum and maximum fluorescence under dark conditions (F0 and Fm, respectively) [49]. ...
... For spectral traits, vegetation indices (VI) calculated from multispectral images acquired by UAV are used for evaluating vegetation properties, such as plant structure, biochemistry, and plant physiological and stress status [31,33,34,[40][41][42][43][44][45][46][47][48]. A large number of VIs have been proposed and have been used in ground-based platforms, aircraft, and satellite remote sensing. ...
Article
High-throughput phenotyping (HTP) allows automation of fast and precise acquisition and analysis of digital images for the detection of key traits in real time. HTP improves characterization of the growth and development of plants in controlled environments in a nondestructive fashion. Marchantia polymorpha has emerged as a very attractive model for studying the evolution of the physiological, cellular, molecular, and developmental adaptations that enabled plants to conquer their terrestrial environments. The availability of the M. polymorpha genome in combination with a full set of functional genomic tools including genetic transformation, homologous recombination, and genome editing has allowed the inspection of its genome through forward and reverse genetics approaches. The increasing number of mutants has made it possible to perform informative genome-wide analyses to study the phenotypic consequences of gene inactivation. Here we present an HTP protocol for M. polymorpha that will aid current efforts to quantify numerous morphological parameters that can potentially reveal genotype-to-phenotype relationships and relevant connections between individual traits.
... Previous studies using UAS data have looked into the mapping of biophysical parameters such as leaf area index (LAI) (Verger et al., 2014;Yao et al., 2017), chlorophyll (Jay et al., 2017), biomass Viljanen et al., 2018), plant density (Jin et al., 2017), and canopy height (Song and Wang, 2019;Ziliani et al., 2018) as well as combinations of these parameters (Jay et al., 2019). However, most UAS studies investigate the mapping of plant traits in monocultural crop stands, while multispecies systems such as natural or cultivated permanent grassland ecosystems like in pre-Alpine regions have been studied less often. ...
Article
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Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.
... Furthermore, NIR produced the largest variation in reflectance throughout the day, especially for flights at 8:00 AM and 4:00 PM. The angle of incident sunlight and its environmental fluctuation can impact the NIR-specific reflectance of crops, such as wheat [26], barley [27] e rapeseed [28], and sugarcane in this study. Other limiting factors may include leaf's components and photosynthetic processes. ...
Article
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Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.
... Remote sensing technology has been used to monitor crop growth parameters such as biomass [20], the leaf area index [21,22], chlorophyll content [23,24], and vegetation indices (VIs) based on the reflectance in different spectral bands. Vegetation indices can be used to simply and effectively express the growth status of vegetation [25] and therefore to estimate crop growth parameters and crop yield. ...
Article
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Crop yields are important for food security and people’s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield.
... Other issues that affect the results of UAV RGB images are factors relating to a differing soil background (Sankaran et al., 2015) and light angle during flight (Verger et al., 2014). Particularly the bidirectional reflectance in single images is not adjusted specifically from the photogrammetry process based on colour-correction in Pix4D (Tu et al., 2018). ...
Article
Spectral correction of colour (RGB) cameras mounted on unmanned aerial vehicles (UAV) is considered important to produce accurate data for quantitative studies in agricultural field research and breeding nurseries. This study investigated the extent to which a simplified spectral correction improves the precision and accuracy of early vigour assessments in winter wheat and winter barley genotypes based on the excess green vegetation Index (nExG) from a pairwise comparison of colour-corrected orthomosaics produced with two different RGB cameras. Two methods of spectral correction were compared, the empirical line method (ELM) and the spectral correction performed in a commercial photogrammetry software package (Pix4D Mapper). Both methods improved the accuracy, with the ELM spectral correction performing better than the Pix4D Mapper spectral correction. Despite high precision and improved accuracy after spectral correction, there were still statistically significant camera effects on vigour assessments because of the minute differences in nExG between genotypes. However, camera effects were evaluated as marginal and with little practical and agronomical relevance under field conditions and in breeding nurseries, as the minute differences between genotypes is overall difficult to reproduce in outdoor field experiments.
... Thirty-five GCPs were positioned by the Trimble GeoExplorer 6000 Series GeoXH (Trimble Navigation, Sunnyvale, CA, USA) within the error of 2.5 cm. A rectangular gray panel (with 8% reflectance) in 3 m 2 was measured in the laboratory for transforming the DN values into reflectance [57]. Radiometric calibration was conducted by following the procedures as described in Jay et al. [21]. ...
Article
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Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted vegetation indices (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis (LSMA) of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACIred-edge showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACIred-edge and AAEVI yielded the highest accuracy (R2=0.78, RMSE=0.26%, rRMSE=10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (R2<0.40). These findings illustrated that the AAVIs have great potential in monitoring nitrogen status at early growth stages with high-resolution aerial or satellite images in the context of precision crop management.
... LAI is an important parameter that indicates crop photosynthesis and growth status. It is of significance for crop yield prediction [1,53]. The situation issue associated with NDVI for dense vegetation has been widely discussed in previous studies [15,54,55]. ...
Article
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Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.
... In particular, unmanned aerial vehicles (UAVs) enable a flexible and cost-effective acquisition of high-spatial resolution image data (Candiago et al., 2015;Colomina and Molina, 2014;Primicerio et al., 2012). Several plant traits have already been determined non-invasively, such as leaf area index (Hunt et al., 2008;Siegmann and Jarmer, 2015;Verger et al., 2014), canopy height (Holman et al., 2016;Madec et al., 2017;Bendig et al., 2013), biomass (Bendig et al., 2015;Hunt et al., 2005;Kross et al., 2015) and lodging (Wilke et al., 2019;Chu et al., 2017). These plant traits are vital for applications in the fields of precision agriculture, breeding research, insurance applications or crop modeling. ...
Article
Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season. The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R² > 0.91, mean absolute error (MAE) < 28 plants). In addition, prediction accuracy only slightly declines for multispectral image data having 1.4 cm GSD or RGB image data having 0.6 cm GSD (MAE < 35 plants m⁻²). BBCH stage 13 was identified as the ideal growth stage in which the plants were large enough to accurately determine fractional cover even from the lower resolution image data. Moreover, a developed empirical regression model was transferred to an independent experimental field verifying its robustness across different conditions. The prediction accuracy of UAV estimated plant density showed an R² value of 0.83 and an MAE of less than 21 plants m⁻². Furthermore, manual measurements of 11 randomly selected plots proved sufficient for a user-based training of the regression model (R² = 0.83, MAE < 23 plants m⁻²) adapted to the independent experimental field. The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.
... These sensors offer very high-resolution images, low operational and maintenance costs and instantaneous data transmission [15]. Among them, multispectral (MS) sensors stand out as one of the best options for assessing crop growth, biomass quantity and several biochemical indicators [16][17][18], specifically from forages [19]. The more common MS sensors are able to register five or six bands in the visible-near infrared spectra region. ...
Article
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The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019-20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm "Gallegos de Crespes" located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.
... The potential of UAVs for conducting detailed surveys in precision agriculture has been demonstrated for a range of applications such as crop monitoring [10,11], field mapping [12], biomass estimation [13,14], weed management [15,16], plant population counting [17][18][19], and spraying [20]. A large amount of data and information is collected by UAVs to improve agricultural practices [21]. ...
Article
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Disease diagnosis is one of the major tasks for increasing food production in agriculture. Alt-hough precision agriculture (PA) takes less time and provides a more precise application of agri-cultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challeng-ing task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and or-thorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extrac-tions to interpret results. Researchers also use the values of vegetative indices, such as Normal-ized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from dif-ferent multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The fu-ture of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance sys-tems, and so on. This review briefly highlights the advantages of automatic detection of plant dis-eases to the growers.
... Diversas vantagens podem ser atribuídas ao uso de RPAs na agricultura. Verger et al. (2014) e Gao et al. (2018), relatam o baixo custo financeiro e alta flexibilidade. ...
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É crescente o uso de sistemas remotos para aplicação de agrotóxicos, entretanto, observa-se a carência de estudos relacionados ao tema. O presente trabalho teve como objetivo desenvolver e avaliar uma aeronave remotamente pilotada (RPA) para a aplicação de agrotóxicos. Inicialmente, projetou-se a RPA, e construiu-se com poliestireno extrudado de 5 mm e isopor t7. Após a construção da RPA foi instalado um sistema de pulverização hidráulica constituído de bomba hidráulica; Tanque com capacidade de 0,350 L e pontas Jacto®, cone vazio, modelo JD12P. Posteriormente, realizaram-se testes de eficiência de aplicação. Montou-se um experimento, em condições de laboratório, em delineamento inteiramente casualizado, em esquema fatorial (3x7), três alturas de voos (1,0; 2,0 e 3,0 m) e sete posições de etiquetas hidrossensíveis no solo (-1,5; -1,0; -0,5; 0,0; +0,5; +1,0 +1,5 m). O pulverizador foi previamente aferido quanto a vazão de líquido e velocidade do voo, o qual apresentaram valores de 0,160 L min-1 por ponta e velocidade do voo de 20 km h-1, respectivamente. As pulverizações foram realizadas nas referidas alturas, na posição 0,0. A referência com sinal negativo trata-se da deposição a esquerda do sentido de deslocamento e sinais positivo deposição a direita do sentido de deslocamento. Após as pulverizações, as etiquetas foram digitalizadas e submetidas a análise. A RPA apresentou melhor características de deposição no alvo, quando operou na altura de voo de 1 m. O sistema possibilita aplicação em áreas localizadas principalmente em plantas perenes de elevado porte.
... Previous studies using UAS data have looked into the mapping of biophysical parameters such as Leaf Area Index (LAI) 90 (Verger et al., 2014;Yao et al., 2017), chlorophyll (Jay et al., 2017), biomass Viljanen et al., 2018), plant density (Jin et al., 2017), canopy height (Song and Wang, 2019;Ziliani et al., 2018) as well as combinations of these parameters (Jay et al., 2019). However, most UAS studies investigate the mapping of plant traits in monocultural crop stands, while multispecies systems such as natural or cultivated permanent grassland ecosystems like in pre-Alpine regions have been studied less often. ...
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Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UAS) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot 15 Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (Linear Model; Random Forests, RF; Gradient Boosting Machines, GBM) and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors, but was not available in our study. Therefore, we tested the added value of this structural information with in-situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in Southern Germany to obtain in-situ and the corresponding 20 spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized and all model setups were run with a six-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor-predictor set combinations with average (avg) R 2 cv of 0.48, RMSE cv, avg of 53.0 g m² and rRMSE cv, avg of 15.9% for DM, and with R 2 cv, avg of 0.40, RMSE cv, avg of 0.48 wt.% and rRMSE cv, 25 avg of 15.2% for plant N concentration estimation. The optimal combination of sensors, ML algorithms and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R 2 cv = 0.67, RMSE cv = 41.9 g m², rRMSE cv = 12.6%) was achieved with a RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of a RF model with all predictors and SEQ sensor data (R 2 cv = 0.47, RMSE cv = 0.45 wt.%, rRMSE cv = 14.2%). DM models with the spectral input of REM performed significantly better than 30 those with SEQ data, while for N concentration models it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy
... This might be related to the high cloud coverage in the Aysén region, which makes satellite acquisition infrequent. Unmanned Aerial Vehicles (UAVs) have the advantage of getting information under clouds and their reasonable spatial coverage makes them an innovative alternative for under-studied areas thanks to their accessibility [14][15][16][17][18][19][20]. Moreover, they are practical and have innovated research on applications for forests and their traits across the globe, therefore, we present UAVs as the potential main tool for the development of new approaches to monitoring the sub-Antarctic forest and its resilience to climate change. ...
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The effects and consequences of global warming on the productivity of the Patagonian forest are still unknown. The use of Unmanned Aerial Vehicles (UAV) promotes new knowledge of the most pristine and unknown sub-antarctic forests located in Chilean Patagonia. This work presents an initial approach to spatialize biochemicals over the Patagonian forests using ultra-high spatial resolution imagery acquired from UAVs equipped with a multispectral (visible, near-infrared, and thermal) sensor. The images were obtained in multiple flights over the Cerro Castillo National Park (Aysén Region, Chile), and several Vegetation Indices (VIs) were estimated. Leaves of Notho-fagus pumilio (Poepp. et Endl.) Krasser (Nothofagaceae) individuals were extracted after the flights and were then used to determine the biochemicals traits of chlorophylls (Chl-a and Chl-b) and ca-rotenoids pigments, as well as the total phenolic content (TPC), total flavonoids content (TFC), and the DPPH radical scavenging assay. Their relationships with multiple VIs was analyzed in order to assess the spatiality of the biochemicals traits in the forest during it most productive phenological stage. Results showed high correlations for the biochemical traits pigments (R 2 > 0.75) with the indices DVI, MCARI, and MSAVI1 as the best performing indices, while further spectral availability is needed for significant correlations with biochemicals traits related to the antioxidant capacity. Spatialization of the biochemical traits within UAV imagery was also performed evaluating their representation in the forest. This work allowed us to identify the different spectral behavior of the N. pumilio species, its relation to biochemical traits, and their spatialization, thus presenting the first step to developing a monitoring protocol for the evaluation of the Patagonian forests under the current global warming scenarios in the region.
... • Multispectral cameras, which provide information in different channels within the visible, near infrared (NIR, from at 0.75 to 1.4 µm) and short-wave infrared (SWIR, 1.4 to 2.4 µm) domains of the electromagnetic spectrum. The reflectance on different spectral bands allows a better characterization of crop biophysical variables like green leaf area index (GAI), or leaf and canopy chlorophyll content, (Blancon et al. 2019;Daughtry 2000;Hunt et al. 2010;Jay et al. 2019;Laliberte et al. 2011;Verger et al. 2014). • Portable spectrometers: Hyperspectral sensors are usually expensive systems capable of observing across several hundred wavelengths with fine bandwidth simultaneously. ...
Thesis
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Cereal crops are the most critical source of food for the world population. The recent advances in plant genomics have generated new opportunities to increase plant genetic variability, with tremendous potential for crop improvement. However, the effective contribution of these advances to increase crop productivity depends on how genotypic traits can be linked with eco-physiological mechanisms that produce a distinguishable response of the genotype to the environment.Plant phenomics –the observation of plant phenotypic traits– is the discipline that must fill the gap between genotype and phenotype. Traditionally, field phenotyping has relied on manual or destructive, low-throughput, observations of phenotypic traits such as plant height, crop stage, and yield components. The development of high-throughput field phenotyping platforms and sensors has opened a new era of plant phenomics. This has an enormous potential impact on the efficiency of breeding programs, as it would enable plant breeders to phenotype large number of genotypes accurately.The advances in computer vision and the introduction of deep learning is transforming several traits previously accessible only through manual sampling into high throughput ones. Thanks to their impressive performance, the rapid adoption of these techniques for field plant phenotyping has progressed rapidly in the last five years. The main challenge for the use of deep learning in operational conditions are linked with the lack of generalization where convolutional neural networks are applied over datasets that differ to some extent –i.e. that belong to a different domain– from the dataset used for training them. Compared to the identification of real-world objects, the implementation of deep learning in field phenotyping still has some specific issues that have not been fully addressed by the existing literature. This thesis studies the use of deep learning techniques for the estimation of three essential traits for plant phenotyping: plant density at early stages for maize, wheat head density, and wheat heading date. The thesis is structured into three chapters that take the form of scientific papers, each one dealing with a specific phenotypic trait, and using a specific vector and detection/counting algorithm.
... Green area of a canopy is involved in key processes including photosynthesis, respiration, and evapotranspiration. It reflects the potential growth of the canopy and is a key variable when modelling biomass production as well as yield and yield loss (Verger et al., 2014). Green Area Index (GAI) is the ratio of green canopy area to the area of ground it covers (Black et al., 2009) and is often assessed indirectly by measurement of the fractional interception by a crop, which together with an assumed extinction coefficient, can be used to predict GAI. ...
Article
Field beans are an up-and-coming crop in Irish agriculture, helping to reduce imports of feed protein and encouraging a home-grown source for cattle feed. Since the introduction of the protein grant in 2015 as part of the Common Agricultural Policy (CAP) greening scheme, the area of field beans sown in Ireland has increased rapidly. However, due to their unpredictable year on year variation in yield, field beans have not yet reached their full potential in Irish agriculture. A better understanding of their growth and development as well as management of the crop for full yield potential is essential to encourage growers to avail of the added benefits of having field beans in their crop rotations. This thesis outlines research which aimed to develop a better understanding of the agronomy and physiology of field beans in the temperate Irish climate, to gather information and create advice for Irish growers on the best way to grow and manage field beans. This was achieved through three years of field experiments from 2017-2019, where different sowing dates, seed rates and varieties were used to vary the canopy size. Through this canopy manipulation, the variation in leaf green area, pods per unit area and seeds per unit area was evaluated in order to identify the key components of yield in field beans. Throughout this research, several parameters were studied. This thesis outlines the results of this study on the effect of sowing date, seed rate and variety on the growth and development of the field bean crop in a temperate climate. The results of this study found that even though field beans show great variability in yield from year to year, with the correct sowing conditions and management, they have the potential to produce high yields in the Irish climate. Using a broad range of seed rates from 10 – 80 seeds per square metre over six sowing dates, the response of field bean yield to these factors could be thoroughly studied over three years. In 2017, yield in this study was found to be 6.2 t ha-1, which was close to the national average yield for field beans of 6.7 t ha-1 (Teagasc, 2018). Yield was found to be the lowest in 2018 when it dropped to 2.5 t ha-1 due to lower-than-average rainfall from pre-flowering to harvest. The October sowing date generally yielded highest for the winter sown treatments and February/March for the spring sown treatments, coinciding with the current recommendations for sowing field beans in Ireland by the Department of Agriculture, Food and Marine (DAFM). However, with the variation found in crop establishment over the three years of study, yield was examined and presented against plant populations instead of seed rate. The general trend showed that as plant populations increased, yield increased. This led to the study of the economic plant population in field beans for Ireland, which we believe to be the first to report. The economic plant population for the spring variety was between 24 – 38 plants per square metre and 13 plants per square metre for the winter variety. This study concluded that yield and profit will not improve by sowing at higher plant densities. Further study into the components of yield in field beans found a strong relationship between pod number and final yield. It was generally found that pod number closely related to the Green Area Index (GAI) of the crop during the pod development phase of growth, which led to the hypothesis that light interception during the pod development phase determined pod number and thereby yield. A supplementary experiment was carried out to support this, where shades were erected over the plots, reducing the intercepted radiation by c.60%. This found that when light was reduced during the reproductive phase, there was a 27% yield reduction, resulting from a 38% reduction in pod number. With green area strongly relating to pod number per square metre, it can be concluded that radiation intercepted during the reproductive phase is crucial for the determination of pod number which in turn is a driving factor in final yield of field beans. Crops like cereals and oilseeds have been studied to determine management strategies for fertilisers and spray treatments throughout the season. Field beans are a relatively new, up and coming crop in Irish agriculture and the knowledge behind field bean management in Ireland is being trialled. This study found that green area in field beans is strongly related to leaf fresh biomass. From this, we hypothesised that leaf fresh biomass can be used as a predictor of GAI and in turn be used by growers as a tool in canopy and overall crop management throughout the season. A model was created using the relationship between leaf green area and leaf fresh biomass, resulting in the equation y = 0.0021x – 0.0734. Using an independent field bean data set and the equation from the model, results showed a strong correlation between measured leaf green area and predicted leaf green area with an R2 = 0.92 and RMSE of 0.38. The greater understanding of yield components and the driver of final yield in field beans will lead to further studies in this area and a greater understanding of this potentially high yielding crop in temperate climates. Overall, the findings in this thesis are a foundation for further research in field beans and other protein crops in Irish agriculture.
... The raw single frames taken concurrently by the six cameras were firstly co-registered to the reference image at 530 nm using the code developed by Rabatel and Labbé (2015). Vignetting effects were then corrected following the procedure proposed by Verger et al. (2014). Agisoft Photoscan software (Version 1.2.4.2399, ...
Thesis
Global agricultural production is facing great challenges due to the growing global population, climate change, decreasing global agricultural land areas and increasing demand on healthy diets. To tackle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. Conventional field survey is usually time-consuming and costly. The emerging digital technologies, such as remote sensing from satellite, unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), Internet of things (IoT) and smart phone, appears as an efficient tool to monitor continuously the crop and its environment from kilometric to centimetric spatial scales and various temporal scales. Interpretation of raw data measured by these remote sensing sensors into traits that are closely related to crop growth status is becoming important in this context. The thesis is focus on leaf area index (LAI) and fraction of absorbed/intercepted photosynthetically active radiation (fAPAR/fIPAR) measured by multiple sources remote sensing platforms.
... As shown in Figure 3, the overlap of UAV red edge and Sentinel RE2 bands was less than the other three bands, so the reflectance of these two bands exhibited the lowest correlations. Further, the bandwidth of the RE channel was very narrow (10 nm), so the signal of this band was not strong enough and therefore might be more easily affected by environmental conditions [46]. Another argument could be that the reflectance at the red edge and NIR ranges usually changed rapidly [30], so the slight difference in central wavelengths and bandwidths could cause an obvious discrepancy in reflectance. ...
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Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0-10 cm and 10-20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.
... An effective way to eliminate the effect of illumination on RGB images is not available [94,95]. Some measures could help in overcoming the effect of illumination on RGB images for long flight or several flights conducted over different days, such as conducting flights under stable or homogenous illumination conditions, making adjustments for varying illumination, and normalizing the RGB images [93,94,96]. Because in this study RGB images were not collected under homogenous illumination conditions, the models were suitable for accurate estimation of CCI under the prerequisite that RGB images are obtained at flight heights between 100 and 120 m and illumination levels of 369-546 W/m 2 . ...
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Leaf chlorophyll content is an important indicator of the physiological and ecological functions of plants. Accurate estimation of leaf chlorophyll content is necessary to understand energy, carbon, and water exchange between plants and the atmosphere. The leaf chlorophyll content index (CCI) of 109 Moso bamboo samples (19 for training data, 19 for validation data, and 71 for extrapolation data) was measured from December 2019 to May 2021, while their corresponding red–green–blue (RGB) images were acquired using an unmanned aerial vehicle (UAV) platform. A method for estimating leaf CCI based on constructing relationships between field leaf CCI measurements and UAV RGB images was evaluated. The results showed that a modified excess blue minus excess red index and 1.4 × H-S in the hue–saturation–value (HSV) color space were the most suitable variables for estimating the leaf CCI of Moso bamboo. No noticeable difference in accuracy between the linear regression model and backpropagation neural network (BPNN) model was found. Both models performed well in estimating leaf CCI, with an R2 > 0.85 and relative root mean square error (RMSEr) < 15.0% for the validation data. Both models failed to accurately estimate leaf CCI during the leaf-changing period (April to May in off-year), with the problems being overestimation in low leaf CCI and underestimation in high leaf CCI values. At a flight height of 120 m and illumination between 369 and 546 W/m2, the CCI for an independent sample dataset was accurately estimated by the models, with an R2 of 0.83 and RMSEr of 13.78%. Flight height and solar intensity played a role in increasing the generality of the models. This study provides a feasible and straightforward method to estimate the leaf CCI of Moso bamboo based on UAV RGB images.
... Moreover, there are also multispectral images from UAVs that serve as a good reference for the determination of seedling emergence, as well as the rise of spring wheat. Recently, some scholars have judged the maturity of wheat, as well as sorghum under drought conditions by UAV-based multispectral indices (Guillen-Climent et al., 2012;Verger et al., 2014;Jin et al., 2017). Hunt et al. (2013) constructed the Green Normalized Difference Vegetation Index (GNDVI) from multispectral images obtained by UAV and inversed the leaf area index of wheat through the vegetation index. ...
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To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (r), root means square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those underwater limitation treatment at different fertility stages; several vegetation indices and CC values showed a highly significant correlation, with the highest correlation reaching.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model (r = 0.63, RMSE = 3.28, NRMSE = 16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model (r = 0.63, RMSE = 3.47, NRMSE = 19.2%).
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Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab, while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables’ spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.
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There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker–trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.
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The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019–2020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R2 value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R2 values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth.
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The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Unmanned aerial vehicle (UAV) platform has been perceived as a useful tool for high-throughput field phenotyping of crop growth traits. While interpretation of UAV image data and retrieval of reliable and accurate phenotypic information are still challengeable due to the variations in sensors, crops and environment conditions. The aim of this study, therefore, is to explore the potential of UAV-based field phenotyping with the PROSAIL model to estimate biomass of rice and oilseed rape crops. Field experiments were designed for rice and oilseed rape with different nitrogen (N) treatments, and a UAV platform mounted with a multispectral camera was used to collect multi-temporal field images. Simultaneously, field measurements of leaf chlorophyll content (Cab), leaf area index (LAI), canopy chlorophyll content (CCC) and biomass were conducted. The results showed that coupling UAV-based multispectral images at the spectral region of 604–872 nm with the PROSAIL model successfully retrieved Cab, LAI and CCC of rice with the root mean square error (RMSE) of 5.40 μg/cm², 1.13, and 43.50 μg/cm², respectively. Further, the Cab, LAI and CCC retrieved from the PROSAIL model achieved the satisfactory biomass estimation in rice with the RMSE of 0.32 kg/m², 0.23 kg/m² and 0.22 kg/m², respectively, which was comparable or superior to those obtained from commonly used empirical models. The proposed method also presented the robust performance for rice biomass estimation at different growth stages. In addition, model validation with the oilseed rape dataset showed an acceptable accuracy of biomass estimation with the determination coefficient (r²), RMSE and relative RMSE of 0.81, 0.03 kg/m² and 27.82%, respectively, and still outperformed the empirical models with the better estimation performance. These findings demonstrate the potential of the proposed biomass retrieval strategy for UAV-based multispectral images, which also extend the application of PROSAIL model in field phenotyping of crop growth traits.
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The aim of the research was to study the production process of rice agrophytocenoses and carry out their geoinformation monitoring to develop a methodology for automated mapping of their condition and forecasting yield. Small varietal differences in the productivity of photosynthesis of plants of intensive and extensive rice varieties on different backgrounds of mineral nutrition were noted. When a closed crop is formed, the nature of the distribution of assimilates over the organs of the plant and the shoot is the main physiological mechanism for the formation of different yields of the studied genotypes and their resistance to the effects of unfavorable environmental factors. With the onset of the flowering phase, the differences in the mass of panicle and stem of shoots in intensive and extensive rice genotypes are very significant. Research has been carried out to study the optical properties of cenoses of varieties and their relationship with the morphophysiological characteristics of plants and yield to monitor the state of their crops. It was shown that the value of the vegetation index (NDVI) has a positive relationship with signs of photosynthetic activity of plants and their nitrogen status. Linear regression equations have been obtained, which make it possible to assess the degree of relationship between yield and vegetation index NDVI.
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With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for three years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.
Chapter
This chapter focuses on a very important aspect of the utilization of unmanned aerial vehicles (simply mentioned as drones) in precision agriculture; the route planning of drones in (spot) sampling operations. In particular, a brief description of the main types of drones used in agriculture along with indicative applications is given based on the relative literature. Subsequently, the challenges that arise from on-field drones routing are discussed by highlighting the commonly adopted approach, namely the Travelling Salesman Problem (TSP). This combinatorial optimization problem is solved by employing algorithms, which can result in optimal or near-optimal solutions. Towards this direction, several algorithms are concisely described. Furthermore, representative demonstrations of drones routing are performed under different scenarios. These scenarios include three different agricultural fields comprising of 50, 83, and 100 visiting points. Several hypotheses are evaluated in silico, by considering the number of drones, the initial and final locations of each route, and various operational constraints. The most efficient results in terms of both distance covered and computation time are presented.
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Unmanned Aerial Vehicle (UAVs) remote sensing has been widely considered recently in field-based crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted in Urmia University, Iran. The study was done using a randomized complete block design at three levels of 60, 80 and 100 percent of irrigation water and nitrogen requirements during four iterations. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia remote sensing sensor during three phases of the plant growth. In the first section, the effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, in the second section, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on NIR and Rededge spectral bands showed a better performance in both sections. Thus, MTCI indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian Model Averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.
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Unmanned aerial vehicles have been developed and applied to support agricultural production management. Compared with piloted aircraft, an Unmanned Aerial Vehicle (UAV) can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision. They can also "fill in the gap" in locations where fixed winged or rotary winged aircraft are not readily available. In agriculture, UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials. Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management. Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well. This paper presents an overview of research involving the development of UAV technology for agricultural production management. Technologies, systems and methods are examined and studied. The limitations of current UAVs for agricultural production management are discussed, as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.
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This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R2 value of 0.567, accounting for 57 percent of the variation of LAISQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).
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Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire processing chain from raw images up to georeferenced reflectance images, digital surface models and biomass estimates. The processing integrates photogrammetric and quantitative remote sensing approaches. We carried out an empirical assessment using FPI spectral imagery collected at an agricultural wheat test site in the summer of 2012. Poor weather conditions during the campaign complicated the data processing, but this is one of the challenges that are faced in operational applications. The results indicated that the camera performed consistently and that the data processing was consistent, as well. During the agricultural experiments, promising results were obtained for biomass estimation when the spectral data was used and when an appropriate radiometric correction was applied to the data. Our results showed that the new FPI technology has a great potential in precision agriculture and indicated many possible future research topics.
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Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.
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The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High‐Resolution Imaging Spectrometer/Project for On‐Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi‐empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.
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The estimation of soil moisture from reflectance measurements in the solar spectral domain (400-2500 nm) was investigated. For this purpose, 18 soils representing a large range of permanent characteristics was considered. Reflectance data were measured in the laboratory during the soil drying process with a high spectral resolution spectroradiometer. Five approaches were compared. The first one was based on single-band reflectance and on the normalization of reflectance data by the reflectance of the corresponding soil under dry conditions. The second and the third approaches were based on either reflectance derivatives or absorbance derivatives. The fourth and fifth approaches were based on the differences of reflectance and absorbance observed in two non-consecutive bands. In the first step, the relationships were calibrated over half the dataset (nine soils) with emphasis on the selection of the most pertinent spectral bands. Results showed that, for the first approach, the bands corresponding to the highest water absorption capacities (1944 nm) yielded the best soil moisture retrieval performances. For the second and third approaches, the bands corresponding to sharp edges of the water absorption features performed better (1834 nm for the reflectance derivatives and 1622 nm for the absorbance derivatives). The fourth and fifth approaches that can be considered as a generalization of the derivative approach when bands are no longer consecutive, the best performances were achieved when the bands were not separated too much. The best overall retrieval performances were achieved with the absorbance derivatives and the difference of absorbance, confirming the non-linear character of the relationship between soil moisture and reflectance. The previously calibrated relations were tested over the evaluation dataset made of the nine remaining soils. It showed additionally that the normalization of reflectance values by that observed under dry conditions was only partly minimizing soil type effects. The best performances for the lowest soil moisture values (
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Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) may provide technologies needed for farmers to maximize the economic and environmental benefits of precision farming. However, most farmers do not have the skills to utilize these technologies effectively.Through a learning community approach led by the Upper Midwest Aerospace Consortium (UMAC), information was shared among scientists, agricultural producers, and data providers. Farmers and ranchers received value-added information derived from AVHRR, MODIS, ETM+, IKONOS, DigitInc's DALSA camera system and Positive Systems' ADAR 5500 digital aerial camera, over four growing seasons. Emphasis has been placed on reducing the time between data acquisition and delivery of value-added products to farmers, developing practical uses for the data and providing basic training so that the end users could understand how to interpret the information. Farmers and ranchers in rural areas were connected via wide-bandwidth satellite link to a central distribution center at the University of North Dakota. The farmers participated actively in evaluating the usefulness of inputs derived from remotely sensed data, sometimes even by conducting experiments on fertilizer and fungicide applications and assessing the economic benefits. Resulting applications included management zone delineation, verifying the effectiveness of variable-rate fertilizer applications, verifying the effectiveness of fungicide applications, quantifying the loss due to accidental spray drift damage, selecting acres within sugar beet fields under the Payment in Kind program, and monitoring physical damage due to insect, inundation, wind and hail. Several other in-field, early season management practices were also reviewed using high-resolution images.
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A surface bidirectional reflectance model has been developed for the correction of surface bidirectional effects in time series of satellite observations, where both sun and viewing angles are varying. The model followes a semiempirical approach and is designed to be applicable to heterogeneous surfaces. It contains only three adjustable parameters describing the surface and can potentially be included in an algorithm of processing and correction of a time series of remote sensing data. The model considers that the observed surface bidirectional reflectace is the sum of two main processes operating at a local scale: (1) a diffuse reflection component taking into account the geometrical structure of opaque reflectors on the surface, and shadowing effects, and (2) a volume scattering contribution by a collection of dispersed facets which simulates the volume scattering properties of canopies and bare soils. Detailed comparisons between the model and in situ observations show satisfactory agreement for most investigated surface types in the visible and near-infrared spectral bands. The model appears therefore as a good candidate to reduce substantially the undesirable fluctuations related to surface bidirectional effects in remotely sensed multitemporal data sets.
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Export Date: 26 October 2012, Source: Scopus, CODEN: PREAF, doi: 10.1007/s11119-012-9263-8, Language of Original Document: English, Correspondence Address: Zarco-Tejada, P. J.; Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, 14004 Córdoba, Spain; email: pzarco@ias.csic.es, References: Alton, P.B., North, P.R., Los, S.O., The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes (2007) Global Change Biology, 13, pp. 1-12;
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A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from similar to 1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5 degrees zenith angle perpendicularly to the row direction. The system is fixed to a horizontal beam supported by a tractor that moves along the micro-plots. About 100 micro-plots per hour were sampled by the system, the data being automatically collected and registered thanks to a centimetre precision geo-location. The green fraction (GF, the fraction of green area per unit ground area as seen from a given direction) was derived from the images with an automatic segmentation process and the reflectance spectra recorded by the radiometers were transformed into vegetation indices (VI) such as MCARI2 and MTCI. Results showed that MCARI2 is a good proxy of the GF, the MTCI as observed from 57 degrees inclination is expected to be mainly sensitive to leaf chlorophyll pigments. The frequent measurements achieved allowed a good description of the dynamics of each micro-plot along the growth cycle. It is characterised by two periods: the first period corresponding to the vegetative stages exhibits a small rate of change of VI with time; followed by the senescence period characterised by a high rate of change. The dynamics were simply described by a bilinear model with its parameters providing high throughput metrics (HTM). A variance analysis achieved over these HTMs showed that several HTMs were highly heritable, particularly those corresponding to MCARI2 as observed from nadir, and those corresponding to the first period. Potentials of such semiautomatic measurement system are discussed for in field phenotyping applications.
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Precision Agriculture is advancing but not as fast as predicted 5years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.
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The theoretical background of modeling the gap fraction and the leaf inclination distribution is presented and the different techniques used to derive leaf area index (LAI) and leaf inclination angle from gap fraction measurements are reviewed. Their associated assumptions and limitations are discussed, i.e., the clumping effect and the distinction between green and non-green elements within the canopy. Based on LAI measurements in various canopies (various crops and forests), sampling strategy is also discussed.
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Most vegetation indices (VI) combine information contained in two spectral bands: red and near-infrared. These indices are established in order to minimize the effect of external factors on spectral data and to derive canopy characteristics such as leaf area index (LAI) and fraction of absorbed photosynthetic active radiation (P). The potentials and limits of different vegetation indices are discussed in this paper using the normalized difference (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI), and transformed soil adjusted vegetation index (TSAVI). The discussion is based on a sensitivity analysis in which the effect of canopy geometry (LAI and leaf inclination) and soil background are analyzed. The calculation is performed on data derived from the SAIL reflectance model. General semiempirical models, describing the relations between VI and LAI or P, are elaborated and used to derive the relative equivalent noise (REN) for the determination of LAI and P. The performances of VIs are discussed on the basis of the REN concept.
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Rapid, reliable and objective estimations of leaf area index (LAI) are essential for numerous studies of atmosphere–vegetation interaction, as LAI is very often a critical parameter in process-based models of vegetation canopy response to global environmental change. This paper reviews current knowledge concerning the use of direct and indirect methods for LAI determination. The value of optical LAI measurements by means of hemispherical photography has already been demonstrated in previous studies. As clumping seems to be the main factor causing errors in indirect LAI estimation, we suggest that the use of a digital camera with high dynamic range has the potential to overcome a number of described technical problems related to indirect LAI estimation. Further testing and defining of a standardised field protocol for digital hemispherical photography is however needed to improve this technique to achieve the standards of an ideal device.
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This study investigates the retrieval performances of LAI using radiative transfer model inversion. The SAIL and GEOSAIL models were used here to evaluate the impact of the complexity of canopy architecture on LAI estimation. The inversion of SAIL or GEOSAIL models was carried out using a look up table technique. Test data sets were generated with SAIL and GEOSAIL to evaluate the performances. Results indicate first that the way the solution is selected within the LUT appeared to be very important. Comparing the inversion using SAIL or GEOSAIL over test cases simulated with SAIL or GEOSAIL show that consistency between the RTM used in the inversion process and the test cases improves the estimation. However, estimation of LAI for complex canopy architecture with non random distribution of leaves (clumping) is difficult when no prior information is available on the targets. However, it is demonstrated that the effective LAI, i.e. the LAI retrieved from the gap fraction measurements assuming. random leaf distribution, is estimated with much better accuracy.
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The objective of this study was to assess the ability to estimate canopy biophysical variables from remote sensing data observed at the top of the canopy in several directions and wavebands within the visible-near infrared domain. The variables considered were the leaf area index, leaf chlorophyll content, the fraction of photosynthetically active radiation absorbed by the canopy and the cover fraction. The SAIL radiative transfer model was inverted using a simple technique based on look-up-tables. The size of the look-up-table, and the number of its elements selected to get a distribution of the solution were first determined. The nadir reflectance in the red and near-infrared bands was considered to evaluate the retrieval performances in terms of the distributions and co-distributions of the solutions. The optimal spectral and directional sampling to estimate the variables considered was investigated. Finally, the impact of spatial heterogeneity on the retrieval performances, the effect of the model assumptions used to generate the look-up table and the effect of radiometric noise were evaluated. These results were discussed in view of the definition of future satellites and the selection of the best measurement configuration for accurate estimation of canopy characteristics.
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