Preharvest seed composition estimation using satellite imaging provides critical information for food security planning and management at a regional scale. As one of the staple crops, soybean plays important role in U.S. economic development. Estimating soybean seed composition is the precondition for improving seed quality and meal content at scale, therefore, maintaining U.S. soy competence in international markets. Traditionally, soybean seed compositions are measured after harvest via wet chemistry analysis, which is time-consuming and expensive. This study presents very first, to the best of our knowledge, satellite remote sensing of soybean seed composition. We demonstrate that WorldView-3 satellite imagery and machine/deep learning is powerful tool to predict seed composition from standing crops.

Predicting the composition of soybean seeds while the plants are growing in the field is very important to understand how different genotypes, field condition and environment influence different seed composition parameters. Knowing this information at global scale is even more important to understand the dynamics of food insecurity and the interaction of seed composition with global environmental changes. This study aims to develop a machine learning-based soybean seed composition model from the fusion of PlanetScope, Sentinel and Landsat satellite images. Although satellite images provide global coverage throughout the year, it suffers from coarser spatial resolution. However, PlanetScope provides four-band (i.e., red, green, blue, and near infrared) multispectral imageries at approximately 3m spatial resolution daily. Alternatively, Sentinel-2B and Landsat-8 have coarser spatial resolution (10 - 30m), they provide enriched spectral resolution. Therefore, the objectives of this study are to 1) fuse the PlanetScope image with corresponding Landsat and Sentinel images, 2) evaluate several machine learning algorithms (e.g., partial least squares, support vector machine, random forest, and deep neural network) to predict protein and oil content of soybean seeds from the fused satellite images. Two soybean fields were established in 2020 and 2021 at Bradford, MO to perform the experiment. Corresponding PlanetScope, Sentinel, and Landsat images were downloaded and processed for the entire growth seasons. Current results indicate that deep neural network provide the best performance in predicting both protein and oil content of soybean. Future step is to assess different fusion algorithms and predict seed composition at regional or global scale.

Panicle traits exhibit quantitative variation controlled by genes, the environment, and their interaction. In switchgrass, a perennial biofuel crop, identification of quantitative trait loci (QTL) and QTL x E interactions controlling panicle architecture could aid breeding efforts and cultivar development by impacting seed productivity. In this study, we evaluate the genetic architecture of panicle traits including panicle length, primary branching number, and secondary branching number in an outcrossing switchgrass population grown across ten field sites in the central United States. We evaluated pleiotropic relationships between panicle traits and flowering time, tiller production and biomass. We also identified environmental factors correlated with QTL x E interactions and potential candidate genes underlying panicle trait QTL in switchgrass. Overall, our multi-environment mixed QTL model detected 18 QTL for panicle traits. Twelve of the QTL exhibited consistent effects (i.e., no QTL x E), and most (4 of 6) of the effects with QTL x E exhibited condition-specific effects. Many of the QTL x E effects were associated with yearly mean temperature and photoperiod. Panicle QTL co-localized with previously identified flowering time QTL and candidate genes associated with flowering, supporting a pleiotropic model of panicle development based on shared developmental genetics and responses to environmental signals.