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Introduction: Growing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA). Methods: The OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a machine learning algorithm (Random Forest) to perform image segmentation and classification. Spatial k-fold cross-validation was employed to assess accuracy. Results and discussion: Results demonstrated good performance, achieving an overall accuracy of approximately 70%, for both RGB and MS-based imagery, with grass and clover classes yielding similar F1 scores, exceeding 0.7 values. The effectiveness of the OBIA procedure and classification was examined by analyzing correlations between predicted clover fractions and dry matter yield (DMY) proportions. This quantification revealed a positive and strong relationship, with R2 values exceeding 0.8 for RGB and MS-based classification outcomes. This indicates the potential of estimating (relative) clover coverage, which could assist breeders but also farmers in a precision agriculture context. [ABSTRACT FROM AUTHOR]

Introduction: Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model. Methods: The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points. Results and discussion: The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications. [ABSTRACT FROM AUTHOR]

Spatio–temporal determination of phenotypic traits, such as height, leaf angles, and leaf area, is important for the understanding of crop growth and development in modern agriculture and crop science. Measurements of these parameters for individual plants so far have been possible only in greenhouse environments using high-resolution 3D measurement techniques, such as laser scanning or image-based 3D reconstruction. Although aerial and ground-based vehicles equipped with laser scanners and cameras are more and more used in field conditions to perform large-scale phenotyping, these systems usually provide parameters more on the plot level rather than on a single plant or organ level. The reason for this is that the quality of the 3D information generated with those systems is mostly not high enough to reconstruct single plants or plant organs. This paper presents the usage of a robot equipped with a high-resolution mobile laser scanning system. We use the system, which is usually used to create high-definition 3D maps of urban environments, for plant and organ-level morphological phenotyping in agricultural field conditions. The analysis focuses on the point cloud quality as well as the system's potential by defining quality criteria for the point cloud and system and by using them to evaluate the measurements taken in an experimental agricultural field with different crops. Criteria for evaluation are the georeferencing accuracy, point precision, spatial resolution, and point cloud completeness. Additional criteria are the large-scale scan efficiency and the potential for automation. Wind-induced plant jitter that may affect the crop point cloud quality is discussed afterward. To show the system's potential, exemplary phenotypic traits of plant height, leaf area, and leaf angles for different crops are extracted based on the point clouds. The results show a georeferencing accuracy of 1–2 cm, a point precision on crop surfaces of 1–2 mm, and a spatial resolution of just a few millimeters. Point clouds become incomplete in the later stages of growth since the vegetation is denser. Wind-induced plant jitters can lead to distorted crop point clouds depending on wind force and crop size. The phenotypic parameter extraction of leaf area, leaf angles, and plant height from the system's point clouds highlight the outstanding potential for 3D crop phenotyping on the plant-organ level in agricultural fields. [ABSTRACT FROM AUTHOR]

Sweetpotato is a crucial crop to guarantee food security in sub‐Saharan Africa, and drought events are considered one of the most critical factors affecting sweetpotato productivity in this region. In this study, airborne imagery based on reflectance (NDVI, CIred‐edge) and canopy temperature minus air temperature (dT) indices was used to characterize sweetpotato genotypes under drought treatments in Mozambique. Two field experiments established in rainy/hot (Trial A) and dry/cool (Trial B) seasons were assessed. In Trial A, 24 genotypes were subjected to early‐ (ESD), mid‐ (MSD) and late‐season (LSD) drought stress treatments and compared against a control. In Trial B, 120 genotypes were subjected to LSD only. The percentage of reduction in vine weight (PRVW) under drought was related primarily to temporal variation of NDVI and CI, regardless of drought treatment and seasons. dT in relation to control (dTAmp) was associated with PRVW in ESD‐Trial A and LSD‐Trial B, whereas under LSD‐Trial A, dTAmp was related to total fresh storage root weight (TRW). During the rainy/hot season, higher TRW reduction was promoted under ESD; however, under LSD, it was possible to identify productive genotypes able to withstand drought stress, highlighting their relevance for drought‐tolerance selection purposes. [ABSTRACT FROM AUTHOR]

IntroductionRemote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model.MethodsThe multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points.Results and discussionThe major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.

Spatio–temporal determination of phenotypic traits, such as height, leaf angles, and leaf area, is important for the understanding of crop growth and development in modern agriculture and crop science. Measurements of these parameters for individual plants so far have been possible only in greenhouse environments using high-resolution 3D measurement techniques, such as laser scanning or image-based 3D reconstruction. Although aerial and ground-based vehicles equipped with laser scanners and cameras are more and more used in field conditions to perform large-scale phenotyping, these systems usually provide parameters more on the plot level rather than on a single plant or organ level. The reason for this is that the quality of the 3D information generated with those systems is mostly not high enough to reconstruct single plants or plant organs. This paper presents the usage of a robot equipped with a high-resolution mobile laser scanning system. We use the system, which is usually used to create high-definition 3D maps of urban environments, for plant and organ-level morphological phenotyping in agricultural field conditions. The analysis focuses on the point cloud quality as well as the system’s potential by defining quality criteria for the point cloud and system and by using them to evaluate the measurements taken in an experimental agricultural field with different crops. Criteria for evaluation are the georeferencing accuracy, point precision, spatial resolution, and point cloud completeness. Additional criteria are the large-scale scan efficiency and the potential for automation. Wind-induced plant jitter that may affect the crop point cloud quality is discussed afterward. To show the system’s potential, exemplary phenotypic traits of plant height, leaf area, and leaf angles for different crops are extracted based on the point clouds. The results show a georeferencing accuracy of 1–2 cm, a point precision on crop surfaces of 1–2 mm, and a spatial resolution of just a few millimeters. Point clouds become incomplete in the later stages of growth since the vegetation is denser. Wind-induced plant jitters can lead to distorted crop point clouds depending on wind force and crop size. The phenotypic parameter extraction of leaf area, leaf angles, and plant height from the system’s point clouds highlight the outstanding potential for 3D crop phenotyping on the plant-organ level in agricultural fields.

Summary: Tree architecture shows large genotypic variability, but how this affects water‐deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high‐throughput field phenotyping and genome‐wide association studies (GWAS) on an apple tree (Malus domestica) core‐collection.We used terrestrial light detection and ranging (T‐LiDAR) scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)‐by‐SNP and multi‐SNP methods.Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well‐watered and water deficit conditions, suggesting control of water loss was largely genotype‐dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait.We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted. [ABSTRACT FROM AUTHOR]

Keywords: remote sensing; high-throughput field phenotyping; unmanned aerial vehicles (UAVs); multi-sensor synergies; open-data standards; vegetation traits; radiative transfer models (RTM); smart farming EN remote sensing high-throughput field phenotyping unmanned aerial vehicles (UAVs) multi-sensor synergies open-data standards vegetation traits radiative transfer models (RTM) smart farming 1 7 7 10/29/21 20211022 NES 211022 Introduction Sustainable and resilient agriculture with a low impact on the environment is pivotal to ensure food security for a growing global population. Better estimating the plant N by combining information on plant physiology from plant phenotyping with new hyperspectral sensors giving a near-real-time estimation can provide input for optimized management strategies to reduce N applications and thus protect the water resources. Bridging the Gap Between Remote Sensing and Plant Phenotyping - Challenges and Opportunities for the Next Generation of Sustainable Agriculture Remote sensing, high-throughput field phenotyping, unmanned aerial vehicles (UAVs), multi-sensor synergies, radiative transfer models (RTM), open-data standards, vegetation traits, smart farming. [Extracted from the article]

• Canopy temperature (CT) and canopy greenness were measured for a large set of wheat genotypes throughout the grain filling phase. • Temporal trends in CT had a high repeatability and were largely independent of canopy structure and phenology. • A combination of trends in CT and canopy greenness holds the potential to discriminate between cosmetic and functional stay-green. The ability to avoid dehydration is a drought resistance mechanism becoming increasingly more important even in temperate regions. In wheat, dehydration avoidance can be associated with a maintained canopy cooling during dry periods. However, in an average year under temperate conditions, drought periods are rather short which makes it difficult to routinely screen for drought avoidance using canopy temperature (CT). Furthermore, confounding factors such as differences in height, shoot biomass, canopy structure and phenology complicate the interpretation of differences in CT. We aimed to use temporal trends in CT and canopy greenness during short-term drought and heat events in the early grain filling phase to circumvent these problems. During this phase, evaporative demand is high and phenology-driven senescence has yet little effect on CT. Diverse sets of 354 and 71 wheat genotypes where grown in the field phenotyping platform of ETH Zurich in 2018 and 2019, respectively. CT was repeatedly measured during early grain filling by means of drone-based imaging. The temporal trends in CT during early grain filling showed a moderate to high within-year heritability (h2 = 0.35 and h2 = 0.88 in 2018 and 2019, respectively). These trends were largely independent of confounding factors when compared to single time point measurements and likely represent genotype-specific reactions to decreasing water availability more directly than absolute CT. CT trends were also largely independent of the temporal trends in stay-green indices. Therefore, we used a combination of time-resolved CT and stay-green trends to identify genotypes combining both traits. Significant differences were observed in the combined time-resolved index among three replicated check varieties. We therefore propose to use the combined time-resolved index to identify genotypes with improved drought avoidance and functional stay green. [ABSTRACT FROM AUTHOR]