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A geostatistical methodology is presented to optimise the dosage of plant protection products (PPP) in vineyards with spatial variability. Sprayers are commonly used in viticulture to apply a constant volume rate per unit ground area (l ha−1). This can be a problem in vineyard plots with remarkable spatial variability in vine vigour, being necessary guiding winegrowers through a decision-making tool to determine an appropriate uniform volume rate. The leaf area index (LAI), measured by a terrestrial LiDAR scanner at high spatial resolution along crop rows, can be used to determine the optimum volume application rate. The proposed method is based on obtaining different probability maps of LAI by applying an indicator kriging to the original LAI data. As a result, this method allows winegrowers to i) map and locate areas within the plot that, within a given confidence level (70% or 90%), exceed or do not exceed different values (percentiles) of the original LAI, and ii) set the LAI and the corresponding volume rate seeking, for example, to balance the probability (risk) of areas with lower and higher doses than required. In more conservative protection strategies, the method also allows farmers to set the values of LAI and volume rate that greatly minimise the probability of vulnerable areas being underdosed.
Spray drift generated in the application of plant protection products in tree crops (3D crops) is a major source of environmental contamination, with repercussions for human health and the environment. Spray drift contamination acquires greater relevance in the EU Southern Zone due to the crops structure and the weather conditions. Hence, there is a need to evaluate spray drift when treating the most representative 3D crops in this area. For this purpose, 4 spray drift tests, measuring airborne and sedimenting spray drift in accordance with ISO 22866:2005, were carried out for 4 different crops (peach, citrus, apple and grape) in orchards of the EU Southern Zone, using an air-blast sprayer equipped with standard (STN) and spray drift reduction (DRN) nozzle types. A further 3 tests were carried out to test a new methodology for the evaluation of spray drift in real field conditions using a LiDAR system, in which the spray drift generated by different sprayer and nozzle types was contrasted. The airborne spray drift potential reduction (DPRV) values, obtained following the ISO 22866:2005, were higher than those for sedimenting spray drift potential reduction (DPRH) (63.82%-94.42% vs. 39.75%-69.28%, respectively). For each crop and nozzle type combination, a sedimenting spray drift model was also developed and used to determine buffer zone width. The highest buffer width reduction (STN vs DRN) was obtained in peach (˃75%), while in grape, citrus and apple only 50% was reached. These results can be used as the starting point to determine buffer zone width in the countries of the EU Southern Zone depending on different environmental threshold values. Tests carried out using LiDAR system demonstrated high capacity and efficiency of this system and this newly defined methodology, allowing sprayer and nozzle types in real field conditions to be differentiated and classified.
Spray drift is one of the main pollution sources identified when pesticides are sprayed on crops. In this work, inorder to simplify the evaluation of hollow-cone nozzles according to their drift potential reduction, several modelscommonly used were tested by three indirect methods: phase Doppler particle analyser (PDPA) and two differentwind tunnels. The main aim of this study is then to classify for thefirst time these hollow-cone nozzle models all ofthem used in tree crop spraying (3D crops). A comparison between these indirect methods to assess their suitabilityand to provide guidelines for a spray drift classification of hollow-cone nozzles was carried out. The results showthat, in general terms, all methods allow hollow-cone nozzle classifications according to their drift potential reduc-tion (DPR) with a similar trend. Among all the parameters determined with the PDPA, theV100parameter performedbest in differentiating the tested nozzles among drift reduction classes. In the wind tunnel, similar values were ob-tained for both sedimenting and airborne drift depositions. TheV100parameter displayed a high correlation (up toR2= 0.948) with the drift potential tested with the wind tunnel. It is concluded that in general, the evaluated indirectmethods provide equivalent classification results. Additional studies with a greater variety of nozzle types are re-quired to achieve a proposal of harmonized methodology for testing hollow-cone nozzles.
This article contains data related to the research article entitle “Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities” . The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.
Fruit load estimation at plot level before harvest is a key issue in fruit growing. To face this challenge, two sampling methods to estimate fruit load in a peach tree orchard were compared: simple random sampling (SRS) and ranked set sampling (RSS). The study was carried out in a peach orchard (Prunus persica cv. 'Platycarpa') covering a total area of 2.24 ha. Having previously sampled the plot systematically to cover the entire area (104 individual trees or sampling points), both sampling methods (SRS and RSS) were tested by taking samples from this population with varying sample sizes from N = 4 to N = 12. Since RSS requires ancillary information to obtain the samples (ranking mechanism), several proximal and remote sensors POSTPRINT of the article: Uribeetxebarria, A., Martínez-Casasnovas, J.A., Tisseyre, B., Guillaume, S., Escolà, A., Rosell-Polo, J.R., Arnó, J. 2019. Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards. Comput. Electron. Agric. 2 already used or recently introduced in agriculture were assessed as data sources. A total of 14 variables provided by 5 different sensors and platforms were considered as potential ancillary variables. Among them, RGB images captured by an unmanned aerial vehicle (UAV), and used to estimate the canopy projected area of individual trees, proved to be the best of the options. This was shown by the high correlation (R = 0.85) between this area and the fruit load, providing RSS with the UAV-based canopy projected area the lowest Coefficient of Error (CE) for a given tree sample size. Then, comparing relative efficiency between random sampling (SRS) and RSS, the latter enables more precise fruit load estimates for any of the considered sample sizes. Interest and opportunity of RSS can be raised from two points of view. In terms of confidence, RSS managed to reduce the variance of fruit load estimates by about half compared to SRS. Sampling errors above the 10% threshold were always produced significantly fewer times using RSS, regardless of the sample size. In terms of operation within the plot, sample size could be reduced by 50%, from N = 10 for SRS to N = 5 for RSS, and this being expected sampling errors less than 10% in practically 70% of the samplings performed in both cases. In summary, fruit growers can take advantage of the combined use of appropriate data (RGB images from UAV) and RSS to optimize sample sizes and operational sampling costs in fruit growing.
Yield prediction provides valuable information to plan the harvest campaign, fruit storage and sales. Traditionally, yield estimation has been carried out by manual counting of randomly selected samples, without addressing spatial variability within the orchard. To obtain a precise estimation it is necessary to sample a relatively large number of trees, which is unfeasible with manual counting. To solve this issue, this work proposes the use of a Mobile Terrestrial Laser Scanner (MTLS) for fruit detection and yield prediction. Experimental test were carried out in a commercial Fuji apple orchard. The row of threes was scanned from the two sides (east and west). The measurement equipment consisted of an MTLS comprised of a LiDAR sensor, and a real-time kinematics global navigation satellite system (RTK-GNSS) connected to a rugged laptop. The LiDAR sensor used was a Puck VLP-16 (Velodyne LIDAR Inc., San José, CA, USA), which provides a 3D point cloud with calibrated reflectance values of the measured scene. The fruit detection algorithm implemented in this work is divided into four steps: (1) Reflectance thresholding, which delete those points presenting a reflectance lower than 60%; (2) Connected Points Clustering using DBSCAN; (3) Fruit separation, which uses a support vector machine (SVM) to predict the number of fruits that contains each cluster; (4) False positive removal, also based on a trained SVM. From detections obtained with this algorithm, the yield was predicted using a linear model (obtained with training data) that relates the number of detections and the actual number of fruits. Three different trials were evaluated: east (E) side scanning, west (W) side scanning and merging data from both scanned sides (E+W). As it was expected, fruit detection results showed lower detection rates when only scanning from one tree side, presenting detection rates of 38.3% and 48.5% for east and west sides, respectively. However, the detection rate increased up to 75.8% when using E+W data. Similarly, yield prediction results showed higher errors when using data from only one tree side, obtaining a RMSE of 15.2% and 15.3% (east and west, respectively). The prediction improved significantly when using data from both tree sides (E+W), presenting a RMSE of 5.4%. From these results it is concluded that MTLS has potential in yield prediction in fruit orchards. Although fruit detection rates are moderately successful, the system was able to predict the actual number of fruits with low estimation errors. Only using data from one tree side increases the prediction error, but it has de advantage of reducing a 50% the scanning time, which may be interesting depending on the application and the interest of the farmer. Future works will extend this study to other fruit varieties.
Pesticide spray drift poses health hazards to humans and causes a significant impact on the environment. In this work the capacity of an ad hoc light detection and ranging (LiDAR) system to differentiate spray nozzles according to their potential drift risk is evaluated for the first time. A total of 23 drift potential tests using 10 hollow-cone nozzles were carried out with the sprayer kept in a static position. Drift potential reduction (DPR) values of between 88.6% and 93.6% were obtained when comparing standard and drift reduction nozzle types. It was also possible to order different standard nozzle sizes according to their DPR. The LiDAR signal was correlated with several droplet size parameters measured by a phase Doppler particle analyzer (PDPA), being V100 the best indicator. In the four field tests that were performed, the LiDAR system was also able to differentiate between standard and drift reduction nozzles under real application conditions, obtaining a DPR of 56.7%. The results of this work demonstrate that the developed LiDAR system is an advantageous alternative for the assessment of drift potential reduction.
Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, provide the backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This work presents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of the database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. To do so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectance of the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted for use with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of 4.46% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8% when all channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuable information for fruit detection.
Estimation of yield or other fruit quality parameter is of great interest to farmers to decide on management actions just before harvesting and, in any case, to anticipate and plan harvesting operations. Making accurate and reliable estimates often requires systematic sampling that, when covering the whole plot, can result in the use of a large number of samples and a significant effort in time and cost for fruit growers. Faced with this whole area sampling strategy, simple random sampling (SRS) using reduced sample sizes is currently a widely used technique despite the less precise estimates that it provides. In this work, different stratified sampling schemes have been tested to estimate yield (kg/tree), fruit firmness (kg/cm²) and the refractometric index (ºBaumé) in a peach orchard located in Gimenells (Lleida, Catalonia, Spain). In contrast to SRS, the use of ancillary information (NDVI and apparent electrical conductivity, ECa) allowed sampling units or trees to be stratified according to two or three classes (strata) within the plot. The classes or homogeneous stratification zones were delimited by cluster analysis using, either separately or in combination, a multispectral airborne image (NDVI) and a ECa survey map acquired by means of a soil resistivity sensor (Veris 3100). Sampling schemes were then compared in terms of efficiency. In general, stratified sampling showed better results than SRS. Regarding yield estimates, stratified sampling according to two strata of NDVI allowed the sample size to be reduced by 17% compared to the SRS for the same precision. On the other hand, quality parameters may require different stratification strategies concerning the number of strata to be used. While ºBaumé was better-estimated using also stratified samples based on two strata of NDVI, fruit firmness showed better results when stratifying by three classes or strata of NDVI. In any case, neither the ECa nor the combined use of NDVI + ECa have improved sampling efficiency when used as ancillary maps for stratification.
A mobile terrestrial laser scanner was developed for high resolution and high accuracy scanning of orchards. In this communication the first results of the data processing and analysis is presented after scanning an olive tree orchard. Specifically, canopy height information is extracted and mapped allowing the spatial representation of this parameter in sets of data representing 10 cm sections and 1 m sections along the rows. The obtained maps will help farmers to detect growth problems in orchards and to improve its management.
Agricultural aerosol emissions can significantly impact human and animal health as well as the environment. Therefore, it is essential to adopt new sensing techniques for real-time monitoring these emissions in high temporal and spatial resolution. In recent years, light detection and ranging (lidar) technology has been used for measuring the particulate matter emitted from agricultural operations. However, conventional non-polarized lidar systems cannot discriminate between different types of aerosols, which can lead to misinterpretation of the results. To overcome this limitation, this study applies the polarization lidar technique to monitor agricultural aerosols. A 355 nm polarization lidar system was used to measure the emissions generated during pesticide spraying operations. The results showed that depolarization ratios due to field dust (0.220-0.268) and to road dust (0.385) are clearly higher than those caused by pesticide spray drift (0.028-0.043) or by diesel exhaust (0.099), which can be used to differentiate each type of aerosol. These results support the development of new polarization lidar systems specifically designed to study the impact of agricultural activities on air quality.
Comparison of situations before (a) and after (b) land transformation, and (c) effects in the spatial variability of apparent electrical conductivity were stone-wall terraces were removed.
The change from traditional to a more mechanized and technical agriculture has involved, in many cases, land transformations. This has supposed alteration of landforms and soils, with significant consequences. The effects of induced soil variability and the subsequent implications in site-specific crop management have not been sufficiently studied. The present work investigated the application of a resistivity soil sensor (Veris 3100), to map the apparent electrical conductivity (ECa), and detailed multispectral airborne images to analyse soil and crop spatial variability to assist in site-specific orchard management. The study was carried out in a peach orchard (Prunus persica (L.) Stokes), in an area transformed in the 1980 decade to change from rainfed arable crops to irrigated orchards. A total of 40 soil samples at two depths (0–30 cm and 30–60 cm) were analysed and compared to ECa and the normalised difference vegetation index (NDVI). Two types of statistical analysis were performed between ECa or NDVI classes with soil properties: a linear correlation analysis and multivariate analysis of variance (MANOVA). The results showed that the land transformation altered the spatial distribution and continuity of soil properties. Although a relationship between ECa and peach tree vigour could be expected, it was not found, even in the case of trees planted in soils with salts content above the tolerance threshold. Two types of management zones were proposed: a) zones delineated according to ECa classes to leach salts in the high ECa zones, and b) zones delineated according to NDVI classes to regulate tree vigour and yield. These strategies respond to the alteration of the original soil functions due to the land transformation carried out in previous years.
Fruit production is relevant to the European agricultural sector. However, orchards in semi-arid areas of southern Europe may contain soils with constrains for tree development. This is the case of soils with high CaCO 3 content or limiting layers at variable depth. To assess spatial and in-depth variation of these soil constraints, an apparent electrical conductivity (ECa) survey was conducted in an orchard by using a galvanic contact soil sensor (Veris 3100). Different soil properties were randomly sampled at two depths (topsoil and subsoil) in 20 different sampling points within the plot. ECa raster maps were obtained for shallow (0-30 cm) and deep (0-90 cm) soil profile depths. In addition, an inversion modelling software was used to obtain horizontal ECa slices corresponding to 10 cm thick soil layers from 0-10 cm to 80-90 cm in depth. Concordance analysis of ECa slices allowed the soil profile to be segmented into four homogeneous horizons with different spatial conductivity pattern. Then, a multivariate analysis of variance (MANOVA) was key, i) to better interpret the specific soil properties that mainly contributed to the spatial variation of ECa (CaCO 3 and organic mater (OM) contents), and ii) to delimit the soil layer and the specific spatial pattern of ECa that allows potential management areas to be delineated by presenting the same trend in CaCO 3 and OM for topsoil and subsoil simultaneously. Moreover, assessing 3D variation of ECa made it possible to identify different soil areas that, linked to previous earthworks to optimize the parcelling of the farm, are the main cause of spatial variability within the orchard.
The present work investigated the application of detailed airborne images and a resistivity soil sensor (Veris 3100) to detect soil and crop spatial variability to assist in orchard management. The research was carried out in a peach orchard ( Prunus persica ). Soil apparent electrical conductivity (ECa), NDVI from a multispectral image (0.25 m/pixel) and soil properties at 40 sampling points (0–30 cm) were acquired. The ECa was standardized at 25°C. It showed a strong relationship with former landforms, altered by land levelling. A positive correlation of EC 25 with EC 1:5 , water holding capacity at −1500 kPa and soil depth was found. NDVI was correlated only in the textural fractions coarser than clay. Two types of management zones were proposed: a) to improve the water holding capacity of soils and b) to regulate tree vigour and yield.
Different sampling schemes were tested to estimate yield (kg/tree), fruit firmness (kg) and the refractometric index (°Baumé) in a peach orchard. In contrast to simple random sampling (SRS), the use of auxiliary information (NDVI and apparent electrical conductivity, ECa) allowed sampling points to be stratified according to two or three classes (strata) within the plot. Sampling schemes were compared in terms of accuracy and efficiency. Stratification of samples improved efficiency compared to SRS. However, yield and quality parameters may require different sampling strategies. While yield was better estimated using stratified samples based on the ECa, fruit quality (firmness and °Baumé) showed better results when stratifying by NDVI.
One of the key issues in crop protection products to be applied in tree crops is the dose rate adjustment. An inappropriate dose selection could be responsible of the lack of efficacy due to under dosage and a loss in efficiency due to over dosage. Both situations imply economical losses and greater impacts on the environment. A better dose adjustment is possible from a variable rate technology approach by measuring the crown volume and adjusting the dose rate on-the-go according to the variation of the estimated vegetation volume. Tests have been done with a variable rate sprayer prototype and satisfactory results have been achieved. Postprint (published version)
One of the key issues in crop protection products to be applied to tree crops is the dose rate adjustment. An inappropriate dose selection could be responsible for lack of efficacy due to under dosage and a loss in efficiency due to over dosage. Both situations imply economic losses and greater impacts on the environment. A better dose adjustment is possible from a variable rate (VR) technology approach by measuring the crown volume and adjusting the dose rate on-the-go according to the variation of the estimated vegetation volume. Tests have been done with a VR sprayer prototype and satisfactory results have been achieved.
The use of a low-cost tractor-mounted scanning Light Detection and Ranging (LIDAR) system for capable of making non-destructive recordings of tree-row structure in orchards and vineyards is described. Field tests consisted of several LIDAR measurements on both sides of the crop row, before and after defoliation of selected trees. Summary parameters describing the tree-row volume and the total crop surface area viewed by the LIDAR (expressed as a ratio with ground surface area) were derived using a suitable numerical algorithm. The results for apple and pear orchards and a wine producing vineyard were shown to be in reasonable agreement with the results derived from a destructive leaf sampling method. Also, good correlation was found between manual and sensor-based measurements of the vegetative volume of tree-row plantations. The Tree Area Index parameter, TAI, gave the best correlation between destructive and non-destructive (i.e. LIDAR-based) determinants of crop leaf area. The LIDAR system proved to be a powerful technique for low cost, prompt and non-destructive estimates of the volume and leaf-area characteristics of plants.
Lidar sensors are used in high resolution canopy characterization of fruit orchards. Tree height, canopy shape, volume and cross-sectional area, foliar area and density can be estimated from lidar data. Most of the procedures need multiple vertical scans to obtain statistically significant information about trees or whole orchards. Therefore, this information is neither processed nor used in real time. Recently, some work has been done on processing lidar data in real time to implement variable rate technologies in fruit production taking into account intra-row canopy variability. Canopy volume and foliage density or area can be used in real time dose adjustment for plant protection products applications. In this paper, a methodology is described to assess the foliar area contained in a single vertical lidar scanned silhouette and a statistical model is developed to estimate it from lidar data in real time. First results are reported from measurements in a Pyrus communis L. cv. 'Conference' orchard. The initial step is to determine the leaves that intercept the laser beam. Second step is to collect all the impacted leaves and the ones behind them within the cross section. Leaf collection was performed dividing cross sections into two heights for each full vertical scan, so each full scan consisted of 2 partial scans. Leaf area was determined in the laboratory. The statistical analysis consisted in applying a linear multivariate regression model. Two models were built, one for partial scans data set and another for full scans data set. In both models foliar area contained in the profile had a statistically significant linear correlation with canopy height and canopy half cross section (R2=0.77 for partial scans and R2=0.86 for complete scans). Results allow orchard canopy characterization in real time and encourage implementation of variable rate technologies in precision fructiculture/horticulture, especially in precision spraying of plant protection products.
In recent years, LIDAR (light detection and ranging) sensors have been widely used to measure environmental parameters such as the structural characteristics of trees, crops and forests. Knowledge of the structural characteristics of plants has a high scientific value due to their influence in many biophysical processes including, photosynthesis, growth, CO2-sequestration and evapotranspiration, playing a key role in the exchange of matter and energy between plants and the atmosphere, and affecting terrestrial, above-ground, carbon storage. In this work, we report the use of a 2D LIDAR scanner in agriculture to obtain three-dimensional (3D) structural characteristics of plants. LIDAR allows fast, non-destructive measurement of the 3D structure of vegetation (geometry, size, height, cross-section, etc.). LIDAR provides a 3D cloud of points, which is easily visualized with Computer Aided Design software. Three-dimensional, high density data are uniquely valuable for the qualitative and quantitative study of the geometric parameters of plants. Results are demonstrated in fruit and citrus orchards and vineyards, leading to the conclusion that the LIDAR system is able to measure the geometric characteristics of plants with sufficient precision for most agriculture applications. The developed system made it possible to obtain 3D digitalized images of crops, from which a large amount of plant information – such as height, width, volume, leaf area index and leaf area density – could be obtained. There was a great degree of concordance between the physical dimensions, shape and global appearance of the 3D digital plant structure and the real plants, revealing the coherence of the 3D tree model obtained from the developed system with respect to the real structure. For some selected trees, the correlation coefficient obtained between manually measured volumes and those obtained from the 3D LIDAR models was as high as 0.976.
Spray drift is one of the main sources of pesticide contamination. For this reason, an accurate understanding of this phenomenon is necessary in order to limit its effects. Nowadays, spray drift is usually studied by using in situ collectors which only allow time-integrated sampling of specific points of the pesticide clouds. Previous research has demonstrated that the light detection and ranging (lidar) technique can be an alternative for spray drift monitoring. This technique enables remote measurement of pesticide clouds with high temporal and distance resolution. Despite these advantages, the fact that no lidar instrument suitable for such an application is presently available has appreciably limited its practical use. This work presents the first eye-safe lidar system specifically designed for the monitoring of pesticide clouds. Parameter design of this system is carried out via signal-to-noise ratio simulations. The instrument is based on a 3-mJ pulse-energy erbium-doped glass laser, an 80-mm diameter telescope, an APD optoelectronic receiver and optomechanically adjustable components. In first test measurements, the lidar system has been able to measure a topographic target located over 2 km away. The instrument has also been used in spray drift studies, demonstrating its capability to monitor the temporal and distance evolution of several pesticide clouds emitted by air-assisted sprayers at distances between 50 and 100 m.
Field measurements of spray drift are usually carried out by passive collectors and tracers. However, these methods are labour- and time-intensive and only provide point- and time-integrated measurements. Unlike these methods, the light detection and ranging (lidar) technique allows real-time measurements, obtaining information with temporal and spatial resolution. Recently, the authors have developed the first eye-safe lidar system specifically designed for spray drift monitoring. This prototype is based on a 1534 nm erbium-doped glass laser and an 80 mm diameter telescope, has scanning capability, and is easily transportable. This paper presents the results of the first experimental campaign carried out with this instrument. High coefficients of determination (R2 > 0.85) were observed by comparing lidar measurements of the spray drift with those obtained by horizontal collectors. Furthermore, the lidar system allowed an assessment of the drift reduction potential (DRP) when comparing low-drift nozzles with standard ones, resulting in a DRP of 57% (preliminary result) for the tested nozzles. The lidar system was also used for monitoring the evolution of the spray flux over the canopy and to generate 2-D images of these plumes. The developed instrument is an advantageous alternative to passive collectors and opens the possibility of new methods for field measurement of spray drift.
This paper describes an instrumental system developed to obtain geometric and structural parameters (phenotyping) of an olive orchard and the subsequent data process for the elabo-ration of raster maps of the parameters of interest. The system consists of a mobile terrestrial laser scanner (MTLS) based on a 2D LiDAR sensor and a RTK GPS, together with a data acquisition system consisting of a laptop computer. The olive orchard under study was an intensive plot of Olea Europaea cv. Arbequina in Torres de Segre (Catalonia, Spain). The studied area was 1 ha and consisted of 16 rows that were scanned from both sides. The wide angular scan window of 270º allowed to simultaneously scan both sides of the olive tree rows while moving along each alley-way. Specific software has been developed to assign the corresponding UTM ETRS89 coordinates to each impact point and to extract various geo-metric parameters of interest for the agronomic management of olive orchards such as can-opy height, width and volume. Additionally, data to build the digital terrain model (DTM) for the study area were also obtained. The maps built showed a clear spatial variability and con-firmed the usefulness of MTLS for the measurement and mapping of relevant vegetative characteristics. This information can be used in the decision making process to decide whether a site-specific management under the framework of precision agriculture could be convenient. This research may be also useful to characterize other agricultural tree crops. Further research should be conducted in order to obtain information on canopy porosity, leaf area and leaf distribution and density taking.
This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of the trunks, PLWA refers to the vertical projected area without gaps detected by LiDAR. As defined, PLWA may be different depending on the side from which the LiDAR is applied. The system is completed by a real-time kinematic global positioning system (RTK-GPS) sensor and an inertial measurement unit (IMU) sensor for positioning. At the end, a total leaf wall area (LWA) is computed and assigned to the X, Y position of each vertical scan. The final value of the area depends on the distance between two consecutive scans (or horizontal resolution), as well as the number of intercepted points within each scan, since PLWA is only computed when the laser beam detects vegetation. To verify system performance, tests were conducted related to the georeferencing task and synchronization problems between GPS time and central processing unit (CPU) time. Despite this, the overall accuracy of the system is generally acceptable. The Leaf Area Index (LAI) can then be estimated using PLWA as an explanatory variable in appropriate linear regression models.
The sustained growth of the world's population in the coming years will require an even greater role for agriculture to meet the food needs of humankind. To improve the productivity and competitiveness of the agricultural industry, it is necessary to develop new and affordable sensing technologies for agricultural operations. This kind of innovations should be implemented in a framework considering the farm, the crops, and their surroundings, with the aim of providing the farmer with information to take better decisions to enhance the production. This is the case of precision agriculture and precision livestock farming. This chapter reviews and discusses the use of structured light sensors in the characterization and phenotyping of crops in orchards and groves, weeds, and animals. As a result of a collaboration between researchers from Spain and Chile, opportunities for this type of sensors have been identified in these countries as examples of South American and European agriculture. In this context, several empirical case studies are presented regarding the use of structured light sensors for flower, fruit, branch, and trunk characterization considering depth and RGB (red-green-blue colors) information in avocados, lemons, apple, and pear orchards. Applications to weed detection and classification as well as to livestock phenotyping are also illustrated. Regarding the presented case studies, experimental and statistical results are provided showing the pros and cons of structured light sensors applied to agricultural environments. Additionally, several considerations are included for the use of this type of sensors to improve the agricultural process.
The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important for improving management decisions and agricultural practices. In this study, a mobile terrestrial laser scanner (MTLS) was used to map the LAI of a vineyard, and then to examine how different scanning methods (on-the-go or discontinuous systematic sampling) may affect the reliability of the resulting raster maps. The use of the MTLS allows calculating the enveloping vegetative area of the canopy, which is the sum of the leaf wall areas for both sides of the row (excluding gaps) and the projected upper area. Obtaining the enveloping areas requires scanning from both sides one meter length section along the row at each systematic sampling point. By converting the enveloping areas into LAI values, a raster map of the latter can be obtained by spatial interpolation (kriging). However, the user can opt for scanning on-the-go in a continuous way and compute 1-m LAI values along the rows, or instead, perform the scanning at discontinuous systematic sampling within the plot. An analysis of correlation between maps indicated that MTLS can be used discontinuously in specific sampling sections separated by up to 15 m along the rows. This capability significantly reduces the amount of data to be acquired at field level, the data storage capacity and the processing power of computers.