Novel implementation of Colby’s method for analyzing interactions between mesotrione and diflufenican using hyperspectral and multispectral machine vision

Abstract

Herbicides play a crucial role in cropping systems by providing effective weed control strategies that help farmers eliminate yield-reducing weeds. However, crop injury may result from herbicides applied in current or previous cropping systems, and in some instances, this injury may reduce crop yield. Currently, herbicide related crop injury is commonly determined by subjective visual assessments. Spectral imaging provides an alternative solution, which is high-throughput and non-invasive. In this study, a novel machine vision method utilizing hyperspectral imaging (HSI) and multispectral imaging (MSI) was developed and integrated into Colby’s method—a traditional approach in weed science for analyzing the interaction effects of herbicide mixtures. Mesotrione and diflufenican, both herbicides that cause bleaching symptomology, were applied in this study. Two rounds of field experiments were conducted in the summer of 2024, where hyperspectral and multispectral images were collected 26 DAT in each trial. Partial Least Squares Discriminant Analysis (PLS-DA) models were built to identify soybean injury from mesotrione, diflufenican, and the mixture. For Colby’s method to study the interaction effect, spatial-spectral features were generated from MSI. The HSI models achieved an accuracy exceeding 90 %. Thirteen distinct features were identified and selected to illustrate the synergistic effects of the herbicides, showing consistency across two experimental rounds and aligning with findings from traditional methods.

Plant phenotyping relevance

除草剤によるダイズ傷害という植物状態を、HSI/MSIと機械学習で客観的・高スループットに推定する手法を開発し、実験間で検証しているため、植物フェノタイピング手法が中心です。

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