Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels.

Abstract

Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum . In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R 2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.

Plant phenotyping relevance

小麦粒のFHB損傷・重症度をハイパースペクトル画像と機械学習で分類・定量する手法が研究の中心であり、Mask R-CNNによる抽出と精度評価も含むため、植物病害表現型の方法研究として適格。

Code and data availability

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