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[Display omitted] Soybean is a pivotal agricultural commodity around the world, primarily because of its high seed protein and oil concentration. Therefore, farmers, breeders and end-users are highly interested in understanding and predicting the soybean seed composition traits from the individual field level or agroecosystem. Seed composition traits are the proportions of different chemical and physical makeup of soybean seeds. Frequent daily coverage of PlanetScope (PS) satellite provides a unique opportunity of estimating seed composition due to its ability to track crop growth and development with its unique combination of high spatial and temporal resolution. We aim to predict six different soybean seed composition traits (i.e., protein, oil, sucrose, fiber, ash, starch) using PS imagery of standing soybean crops and machine learning algorithms. We developed multi-stream deep neural network which is based on two types of recurrent neural networks, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) that utilize temporal phenology observed from PS. Four statistical machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVR) were used for comparison. Our results show that GRU worked well for protein (R2 0.36, NRMSE 3.62%) and oil (R2 0.53, NRMSE 4.78%), SVR showed the best results for sucrose (R2 0.74, NRMSE 8.34%), fiber (R2 0.21, NRMSE 4.20%), and starch (R2 0.15, NRMSE 16.84%), and PLSR provided the best result for ash (R2 0.60, NRMSE 1.70%). Among the features, vegetation indices at later reproductive stages were found as the most important variables compared to texture features. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. [ABSTRACT FROM AUTHOR]

Agricultural management at field-scale is critical for improving yield to address global food security, as providing enough food for the world's growing population has become a wicked problem for both scientists and policymakers. County- or regional-scale data do not provide meaningful information to farmers who are interested in field-scale yield forecasting for effective and timely field management. No studies directly utilized raw satellite imagery for field-scale yield prediction using deep learning. The objectives of this paper were twofold: (1) to develop a raw imagery-based deep learning approach for field-scale yield prediction, (2) investigate the contribution of in-season multitemporal imagery for grain yield prediction with hand-crafted features and WorldView-3 (WV) and PlanetScope (PS) imagery as the direct input, respectively. Four WV-3 and 25 PS imagery collected during the growing season of soybean were utilized. Both 2-dimensional (2D) and 3-dimensional (3D) convolution neural network (CNN) architectures were developed that integrated spectral, spatial, temporal information contained in the satellite data. For comparison, hundreds of carefully selected spectral, spatial, textural, and temporal features that are optimal for crop growth monitoring were extracted and fed into the same deep learning model. Our results demonstrated that (1) deep learning was able to predict yield directly using raw satellite imagery to the extent that was comparable to feature-fed deep learning approaches; (2) both 2D and 3D CNN models were able to explain nearly 90% variance in field-scale yield; (3) limited number of WV-3 outperformed multi-temporal PS data collected during entire growing season mainly attributed to RedEdge and SWIR bands available with WV-3; and (4) 3D CNN increased the prediction power of PS data compared to 2D CNN due to its ability to digest temporal features extracted from PS data. [ABSTRACT FROM AUTHOR]

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVM VI) for soybean [ Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVM VI , which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVM VI , which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVM VI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVM VI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVM VI) for estimations of soybean AGB. CVM VI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management. [ABSTRACT FROM AUTHOR]