Estimation of grain yield in wheat using source –sink datasets derived from RGB and thermal infrared imaging

This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield, and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages. AbstractTimely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source –sink-level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High-quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using pr oximal sensing technology over a 2-year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and sele ction operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The sou...
Source: Food and Energy Security - Category: Food Science Authors: Tags: ORIGINAL ARTICLE Source Type: research