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.