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... For each pixel of the resampled orthoimage (1 m spatial resolution), a cost function was generated to calculate the RMSE between measured surface reflectance and simulated reflectance in the LUT (Equation (1)). The relative reflectance with the NIR band was used in order to minimize the illumination variation between calibration and flight acquisitions (Verger et al., 2014). ...
... However, changing the model on cloudy pixels has small influences on small LAIs of rapeseed (LAI < 5, Figure 5d). This is consistent with the results observed by [12]; that the illumination has minor effects on the inversion of small LAI when the normalization in the cost function is used. The cloudy pixels still show overestimation to some extent at high LAI values (LAI > 5), even if the diffuse model is used. ...
... influences on small LAIs of rapeseed (LAI < 5, Figure 5d). This is consistent with the results observed by [12]; that the illumination has minor effects on the inversion of small LAI when the normalization in the cost function is used. The cloudy pixels still show overestimation to some extent at high LAI values (LAI > 5), even if the diffuse model is used. ...
Article
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Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.
... Canopy parameters can be categorized into three types: biophysical variables [3], [4], [5], such as leaf area index (LAI), net primary productivity, and absorbed photosynthetically active radiation; forest structural variables, such as canopy closure, canopy height, and canopy diameter; and biochemical variables, such as chlorophyll content, total phosphorus content, and carotenoid content. Among them, LAI and canopy chlorophyll content (CCC) are closely related to the plant's ability to intercept incoming photosynthetically active radiation [6], [7]. They are key variables for photosynthesis, respiration, and transpiration, and serve as reliable indicators of various abiotic and biotic stresses. ...
... According to the Pearson correlation coefficient analysis, the relationships between LAI and CCC and different spectral preprocessing methods were calculated. Fig.4(a) shows that in the original spectrum, all bands exhibited highly significant relationships with LAI (p<0.01), with bands B 6 (Table V). Except for RVI, the selected vegetation indices all reached a highly significant level (p<0.01) in terms of correlation with LAI. ...
Article
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Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (Phyllostachys pubescens) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared to the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R2 values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation.
... 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.
... 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. ...
Chapter
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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.
... 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.
... 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/ ...
Article
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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.
... 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 ...
Article
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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.
... UAVs provide valuable data for plant selection across various species [7,8]. They are effective in monitoring key growth parameters, such as biomass [9], leaf area index [10,11], chlorophyll content [12], vigor, and yield [13], all of which can be estimated using vegetation indices [14]. ...
Article
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Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (r = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ r ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model ( = 0.26 at 20 m and = 0.44 at 60 m), while plant vigor ranged from = 0.50 at 20 m to = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs.
... Such platform will target mainly the phenotyping of response traits (LE, TR) but architectural trait will also be evaluated. Some phenotyping methods might also be directly usable on MET used for variety evaluation, such as using unmanned aerial vehicles and image analysis to estimate phenological stages or multispectral camera for canopy architecture (Baret and Buis, 2008;Verger et al., 2014). ...
Preprint
Assessing the performance and the characteristics (e.g. yield, quality, disease resistance, abiotic stress tolerance) of new varieties is a key component of crop performance improvement. However, the variety testing process is presently exclusively based on experimental field approaches which inherently reduces the number and the diversity of experienced combinations of varieties x environmental conditions in regard of the multiplicity of growing conditions within the cultivation area. Our aim is to make a greater and faster use of the information issuing from these trials using crop modeling and simulation to amplify the environmental and agronomic conditions in which the new varieties are tested. In this study, we present a model-based approach to assist variety testing and implement this approach on sunflower crop, using the SUNFLO simulation model and a subset of 80 trials from a large multi-environment trial (MET) conducted each year by agricultural extension services to compare newly released sunflower hybrids. After estimating parameter values (using plant phenotyping) to account for new genetic material, we independently evaluated the model prediction capacity on the MET (model accuracy was 54.4 %) and its capacity to rank commercial hybrids for performance level (Kendall's τ\tau = 0.41, P < 0.01). We then designed a numerical experiment by combining the previously tested genetic and new cropping conditions (2100 virtual trials) to determine the best varieties and related management in representative French production regions. We suggest that this approach could find operational outcomes to recommend varieties according to environment types. Such spatial management of genetic resources could potentially improve crop performance by reducing the genotype-phenotype mismatch in farming environments.
... Near-infrared data encompasses vital information regarding the plants' physiological state and geometric characteristics [50]. These findings align with the research results put forth by Verger et al. [51], further establishing the significance of spectral analysis in the context of agricultural yield prediction. ...
Article
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Timely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underlying yield formation. The study enhances yield estimation by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) and RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle to capture spectral information from rice canopies through MS and RGB sensors. By analyzing the correlations between vegetation indices from different sensors and rice yields, the optimal MS-VIs and RGB-VIs for each period were identified. Following this, the relationship between the cumulative time series of MS-VIs, RGB-VIs, and rice yields was further examined. The results demonstrate that the booting stage is a crucial growth period influencing rice yield, with VIs exhibiting increased correlation with yield, peaking during this stage before declining. For the MS sensor, the rice yield model, based on the cumulative time series of MS-VIs from the tillering stage to the panicle initiation stage, achieves optimal accuracy (R 2 = 0.722, RRMSE = 0.555). For the RGB sensor, the rice yield model, based on the cumulative time series of RGB-VIs from the tillering stage to the grain-filling stage, yields the highest accuracy (R 2 = 0.727, RRMSE = 0.526). In comparison, the multi-sensor rice yield model, which combines the cumulative time series of MS-VIs from the tillering stage and RGB-VIs from the panicle initiation to grain-filling stages, achieves the highest accuracy with R 2 = 0.759 and RRMSE = 0.513. These findings suggest that cumulative time series VIs and the integration of multiple sensors enhance yield prediction accuracy, providing a comprehensive approach for estimating rice yield dynamics and supporting precision agriculture and informed crop management.
... Additionally, UAV images allow to discriminate between different canopy constituents (soil, shaded leaves, sun-lit leaves) and study them separately. UAVs have been used to retrieve crop parameters using parametric (Berni et al., 2009;Kanning et al., 2018), machine learning (Du et al., 2022) and physical models (Verger et al., 2014; , 2022). While there is a considerable research on the retrieval of LAI from UAV-based multispectral or hyperspectral imaging, only a few studies have explored ALIA retrieval (Zou and Mõttus, 2015), particularly in the context of paraheliotropic response of the plants. ...
... The inversion of RTMs enables retrieving leaf (chlorophyll and water content) and canopy (LAI) parameters, which can be further correlated with stress levels (Verrelst et al., 2019). As mentioned earlier, SWIR-carrying UAV systems were developed relatively recently (Jenal et al., 2019;Arroyo-Mora et al., 2021), therefore UAV studies that use RTM inversion are limited to the VIS-NIR domain, sampled either hyperspectrally (Abdelbaki et al., 2021;Duan et al., 2014;Lin et al., 2019;Wan et al., 2021;Wang et al., 2021;Yin et al., 2022) or multispectrally (Antonucci et al., 2023;Chakhvashvili et al., 2022;Jay et al., 2019;Roosjen et al., 2018;Singh et al., 2023;Sun et al., 2021;Verger et al., 2014). As a guideline, we advise to degrade the spatial resolution of a UAV image to satisfy turbid-medium models, such as SAIL, or to mask shaded and bare soil pixels before the inversion (Chakhvashvili et al., 2022). ...
Article
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Introduction Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
... Several vegetation indices (VIs) derived from these bands are often used to evaluate agricultural biophysical parameters. These VIs give detailed information about crop growth and how it reacts to stressors like diseases, pests, changes in soil moisture conditions and temperature, and in estimating crop yield [31], [32]. Examples of these indices include the normalized difference vegetation index (NDVI) [33], the green NDVI (G-NDVI), the normalized difference red edge index (NDRE), and the enhanced vegetation index (EVI) [34], among others. ...
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This study explored how to use UAV-based multi-spectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data was collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices was collected utilizing portable phenotyping instruments, and agronomic data was collected manually. The YOLOv8 detection model was utilized for field scale wheat plot detection. Four ML algorithms–decision tree (DT), random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm's predicting ability across all growth stages, with a root mean square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination ( R2R^{2} ) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an R2R^{2} of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an R2R^{2} value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and machine learning in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices.
... com). The analytical protocol was the same as in previous studies (Verger et al. 2014;Madec et al. 2017). ...
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Key message We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Abstract Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.
... The concept of summarizing spectral data using vegetation indices (VI) was first proposed by Jordan (1969), where one spectral band is standardized by dividing it by a second band. Currently, it is widely accepted that this approach results in more stable calibrations under varying irradiance conditions and VI estimation errors of crop parameters can be reduced (Bukowiecki et al., 2020;Verger et al., 2014). However, it is necessary to identify the optimal wavelengths and combinations of vegetation indices (VIs) for calibrating to both GAI and total N. ...
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The fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat. In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m² m⁻² for GAI and of 0.80–1.21 g m⁻² for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.
... The imaging sensors mounted on the UAV mainly include hyperspectral, RGB, and multispectral sensors. They were reported to have a great performance for monitoring crop growth [11][12][13][14][15][16]. Compared with the former two, UAV-based multispectral sensors can acquire images with a spatial resolution from centimeter to decimeter level near the ground, achieving a better balance between cost and availability [17]. ...
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The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R² = 0.812) and LAI (R² = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R² = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.
... Ferwerda [52] found that the best prediction results were also obtained utilizing continuum-removed reflectance in predicting sodium, potassium, and calcium content in several woody plants. Wu [53], Verger [54], and Yang [23] concluded that in the process of obtaining plant leaf spectra, the absolute value of the reflectance was often underestimated due to unstable lighting conditions or narrow leaves that could not fully cover the window, leading to a decrease in the accuracy of plant potassium content prediction. Therefore, spectral normalized methods were applied to eliminate the effects of differences in incident radiation and leaf width. ...
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The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.
... Capabilities of the unmanned aerial system (UAS) in terms of spatial-time resolution, low cost, ease of use, and application of different sensors have developed this system for improving precision farming (Zhang & Kovacs, 2012). Meanwhile, several research studies have been conducted using UAVs to monitor vegetation conditions, diagnose diseases, examine vegetation stresses, and do phenotyping (Córcoles et al., 2013;Fullana-Pericàs et al., 2022;Ihuoma & Madramootoo, 2017;Verger et al., 2014;Zhao et al., 2018;Zhou et al., 2021). ...
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Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale 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 at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, 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 near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (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.
... In particular, unmanned aerial vehicles (UAVs) enable a flexible and cost-effective acquisition of high-spatial resolution image data [4][5][6]. Several plant traits have already been determined non-invasively, such as leaf area index [7][8][9], canopy height [10][11][12], biomass [13][14][15] and lodging [16,17]. These plant traits are vital for applications in the fields of precision agriculture, breeding research, insurance applications or crop modeling. ...
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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. Using spatially high-resolution images (0.02 cm)recent studies have determined plant density using machine-learning approaches and feature extraction. However the accuracy and practical applicability decreased when only lower resolution images were available. 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. At BBCH stage 13 plants were large enough to determine fractional cover also from the lower resolution image data. The empirical regression models using multispectral images with a ground sampling distance (GSD) of 0.69 cm were also suitable 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).. In the independent experimental field 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 verifying empirical regression model robustness across conditions. 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.
... high accuracy and spatial resolution, and UAV images have been studied as the core data for collecting vegetation biophysical and chemical parameters [11,18,19]. For example, tree species, height, canopy diameter, and above-ground biomass (AGB) forest survey data acquired by UAV with the motion structure and multi-view stereo photogrammetry program (UAV-SfM) can complement and eventually replace traditional forest survey techniques [20][21][22][23]. ...
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The growth of mangroves is inhibited due to environmental degradation, and changes in the growing health of mangrove forests cause changes in internal physicochemical parameters. The canopy chlorophyll content is an important indicator to monitor the health status of mangroves. We study the effective inversion data sources and methods of mangrove health indicator parameters to monitor the health of mangrove ecosystems in typical areas of Beibu Gulf, Guangxi. In this study, we evaluated the capability of UAV, GF-6 data, and machine learning regression algorithms in estimating mangrove species-scale canopy chlorophyll content (CCC). Effective measures for mangrove pest and disease pressure, Sporobolus alterniflorus invasion, and anthropogenic risk are also explored, which are important for mangrove conservation and restoration. (1) We obtained several feature variables by constructing a combined vegetation index, and the most sensitive band of mangrove CCC was selected by the characteristic variable evaluation, and the CCC model at the mangrove species-scale was evaluated and validated. Through variable preferences, the feature variables with the highest contribution of Avicennia marina, Aegiceras corniculatum, Kandelia candel, and a collection of three categories of species in the UAV data were indices of RI35, MDATT413, RI35, and NDI35. (2) Random Forest, Gradient Boosting Regression Tree, and Extreme Gradient Boosting were evaluated using the root-mean-square error and coefficient of determination accuracy metrics. Extreme Gradient Boosting regression algorithms were evaluated for accuracy. In both UAV data and GF-6, RF achieved optimal results in inverse mangrove Aegiceras corniculatum species CCC, with higher stability and robustness in machine learning regressors. (3) Due to the sparse distribution of Kandelia candel in the study area and the low spatial resolution of the images, there is an increased possibility that individual image elements contain environmental noise, such as soil. Therefore, the level of CCC can effectively reflect the health status of mangroves and further reflect the increased possibility of the study area being exposed to risks, such as degradation. The establishment of the current protected areas and restoration of degraded ecosystems are effective measures to cope with the risks of mangrove pest and disease stress, invasion of Sporobolus alterniflorus, and anthropogenic activities, which are important for the protection and restoration of mangroves. This study provides an important data reference and risk warning for mangrove restoration and conservation.
... Remote sensing is an effective and non-destructive method for monitoring plant growth, as it can rapidly and efficiently acquire target components [11]. With the continuous reduction of sensor size and advancements in unmanned aerial vehicle (UAV) technology, UAVs are increasingly being employed for remote sensing data acquisition [12][13][14][15][16]. UAV platforms possess distinct advantages over other remote sensing platforms as they offer cost-effective and adaptable remote sensing imaging capabilities with high temporal and spatial resolutions [17][18][19]. Research on monitoring plant SPAD using UAV platforms has been reported. Zhang Suming et al. [20] utilized a combination of satellite, drone, and ground-based methods to construct a drone inversion model using SPAD values and UAV multi-spectral images. ...
Article
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Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.
... UAV remote sensing technology has recently been widely used in agriculture because of its low cost, high resolution, and fast and repeatable capture capability [5,6]. Compared with ground-based crewless vehicles and high-altitude satellite remote sensing images, UAVs are more suitable for farmland monitoring and are less affected by the atmosphere. ...
Article
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Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton’s late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage.
... Phenotyping methods are able to evaluate plants according to their different traits, such as physiology, yield, development, and tolerance to environmental stresses (Li et al., 2014;Rahaman et al., 2015). Some morphological traits that are often used to evaluate plant growth and characterize the canopy structure include canopy biomass (Hansen and Schjoerring, 2003;Ehlert et al., 2009), height (Zhang andGrift, 2012;Bendig, 2015), and leaf area index (LAI) (Baret et al., 2010;Beĺand et al., 2011;Beĺand et al., 2014;Verger et al., 2014;Zhao et al., 2015). Studies have shown that these morphological traits have a strong relationship with plant genotype, cultivars, growth rate and yield (Sharma and Ritchie, 2015;Friedli et al., 2016;Sun et al., 2018). ...
Article
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Wheat is one of the most widely consumed grains in the world and improving its yield, especially under severe climate conditions, is of great importance to world food security. Phenotyping methods can evaluate plants according to their different traits, such as yield and growth characteristics. Assessing the vertical stand structure of plants can provide valuable information about plant productivity and processes, mainly if this trait can be tracked throughout the plant’s growth. Light Detection And Ranging (LiDAR) is a method capable of gathering three-dimensional data from wheat field trials and is potentially suitable for providing non-destructive, high-throughput estimations of the vertical stand structure of plants. The current study considers LiDAR and focuses on investigating the effects of sub-sampling plot data and data collection parameters on the canopy vertical profile (CVP). The CVP is a normalized, ground-referenced histogram of LiDAR point cloud data representing a plot or other spatial domain. The effects of sub-sampling of plot data, the angular field of view (FOV) of the LiDAR and LiDAR scan line orientation on the CVP were investigated. Analysis of spatial sub-sampling effects on CVP showed that at least 144000 random points (600 scan lines) or an area equivalent to three plants along the row were adequate to characterize the overall CVP of the aggregate plot. A comparison of CVPs obtained from LiDAR data for different FOV showed that CVPs varied with the angular range of the LiDAR data, with narrow ranges having a larger proportion of returns in the upper canopy and a lower proportion of returns in the lower part of the canopy. These findings will be necessary to establish minimum plot and sample sizes and compare data from studies where scan direction or field of view differ. These advancements will aid in making comparisons and inform best practices for using close-range LiDAR in phenotypic studies in crop breeding and physiology research.
... With the advent of newer optical, thermal, and laser sensors, the use of small UASs has grown exponentially in the past few years (van der Wal et al. 2013;Laliberte, Winters, and Rango 2011;Verger et al. 2014;Rasmussen et al. 2013). Applications of UAS-borne sensors include crop acreage estimation (Atkins 2014), crop progress assessment (Geipel, Link, and Claupein 2014), evaluation of efficiencies of fertilizer and/or pesticide applications (Ladd and Bland 2009), disease detection (Mahlein 2016;Kerkech, Hafiane, and Canals 2020), and cultivar evaluations (Gracia-Romero et al. 2019). ...
Article
With the increasing use of unmanned aerial systems (UASs) in the agricultural domain, ensuring the consistency and completeness of aerial surveys is critical in order to establish repeatability and consistency in data collection activities. This publication covers five main steps to ensure that aerial data collections are repeatable and consistent among missions. It is one of a three-part series focusing on the applications, configuration, and best practices for using UASs in agricultural operations management. Written by Aditya Singh and James Fletcher, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2021.
... 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.
... 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
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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.
... 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 . ...
Article
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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. ...
Article
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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%).
... 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.
... Moreover, UAVs have also been utilized as a tool for various vegetation indices extraction. Furthermore, in [20] an algorithm was developed in order to estimate Green Area Index (GAI) from images being taken from cameras of four different bands. By using Root Mean Square Error (RMSE) as a measure of uncertainty, the estimated GAI over rapeseed and wheat crops was close to 0.2. ...
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.
... 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.
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Chapter
The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed.
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Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G×E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G×E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments and their time-series soil moisture was measured. The models were evaluated using leave-one-environment-out cross-validation, in which a treatment-by-year combination was considered the environment. These results suggest that our model can capture G×E during the early growth, especially canopy height. Significant variations in the G×E of the canopy height during the early growth period were visualized using the estimated reaction norms. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G×E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data obtained with remote sensing.
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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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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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https://www.routledge.com/The-Digital-Age-in-Agriculture/Ozguven/p/book/9781032385808
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The reflectance of wheat crops provides information on their architecture or physiology. However, the methods currently used for close-range reflectance computation do not allow for the separation of the wheat canopy organs, the leaves and the ears. This study details a method to achieve high-throughput measurements of wheat reflectance at the organ scale. A nadir multispectral camera array and an incident light spectrometer were used to compute bi-directional reflectance factor (BRF) maps. Image thresholding and deep learning ear detection allowed for the segmentation of the ears and the leaves in the maps. The results showed that the BRF measured on reference targets was constant throughout the day but varied with the acquisition date. The wheat organ BRF was constant throughout the day in very cloudy conditions and with high sun altitudes but showed gradual variations in the morning under sunny or partially cloudy sky. As a consequence, measurements should be performed close to solar noon and the reference panel should be captured at the beginning and end of each field trip to correct the BRF. The method, with such precautions, was tested all throughout the wheat growing season on two varieties and various canopy architectures generated by a fertilization gradient. The method yielded consistent reflectance dynamics in all scenarios.
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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 ~1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5° 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° 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 semi-automatic 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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A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.
Conference Paper
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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 combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain. PROSAIL has also been used to develop new methods for retrieval of vegetation biophysical properties. It links the spectral variation of canopy reflectance, which is mainly related to leaf biochemical contents, with its directional variation, which is primarily related to canopy architecture and soil/vegetation contrast. This link is key to simultaneous estimation of canopy biophysical/structural variables for applications in agriculture, plant physiology, or ecology, at different scales. PROSAIL has become one of the most popular radiative transfer tools due to its ease of use, general robustness, and consistent validation by lab/field/space experiments over the years. However, PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels. This paper provides an extensive review of the PROSAIL developments in the context of canopy biophysics and radiative transfer modeling
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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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Precision Agriculture and crop management at the sub-field level is an application area where satellite imagery can bring significant contributions to help farmers in near real time management decisions. After many years of research and development in partnership with French technical agronomic institutes (ARVALIS for cereals, CETIOM for oil seed rape or ITB for sugar-beet), the New Application Product division of EADS Astrium the major space company in Europe has successfully launched an operational FARMSTAR service. This service is based on an efficient combination of remote sensing and agronomy expertise to provide farmers with satellite imagery based recommendation maps to apply their nitrogen and/or chemicals, to detect stress and to organise their field scouting. The key point is the ability to extract from the satellite images, biophysical parameters such as Leaf Area Index (LAI) or chlorophyll content at specific growth stages of the crop and to introduce them into agronomic models to generate recommendations. This service, largely validated before being commercially launched in France, grew very rapidly from 4000 ha subscriptions in 2002 to nearly 200000 ha on 16000 fields with 6000 farmers in 2005. Commercial deployment has also started in UK and Germany and other countries (Spain, Canada, and Australia) are evaluating the concept.
Chapter
The diffuse reflection of radiation from different media has a sharp maximum in the backward direction. This phenomenon is known as heiligenschein in meteorology, the opposition effect in astronomy, and the hot spot effect in aerial photography and optical remote sensing. These three effects are caused by the same physical mechanisms, and hence are essentially equivalent. If the particles of the reflecting/scattering medium cast shadows, then the shadows cannot be seen looking along the incident rays since they are screened by the particles themselves. With a change in the view direction we can see some of the shadows. Therefore, the mean radiance of reflection decreases. Generally, the radiance of the reflecting medium will decrease with increasing angle α between the view direction and incident rays because of the decreased probability of seeing illuminated particles.
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Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.
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Substantial research has been conducted to derive Leaf Area Index (LAI), an essential climate variable, from satellite imageries acquired by moderate resolution optical sensors. The Medium Resolution Imaging Spectrometer (MERIS) is unique among such sensors in that it provides relatively high spectral (15 bands) and spatial (~300m resolution) sampling within visible and near infrared wavelengths. A recent evaluation of four operational MERIS LAI algorithms found that they did not consistently meet accuracy targets typical of operational requirements. One explanation for the mixed performance of these algorithms may be that they do not suitably exploit the enhanced spectral sampling of MERIS. We exploit this enhanced spectral sampling to estimate several (80) narrow-band vegetation indices (VIs) by interpolating MERIS surface reflectance. The interpolation accuracy was evaluated using Hyperion imagery. Regressions were then calibrated between estimated VIs and in-situ LAI over a range of land cover types. The strongest performance (root mean squared error