A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants

Abstract

Recent years have seen significant technological advancements in precision farming and plant phenotyping. Remote sensing along with deep learning (DL) techniques can increase phenotyping efficiency and help on-farm decision making with rapid stress detection. In this work, we use these techniques to evaluate drought stress in soybean plants, a crop whose yield is significantly affected by water availability. Images were taken from a high vantage in the field at various times throughout the day. Each image is given a wilting score ranging from 0 to 4 by expert scorers. We implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image. Given the significant overlap between adjacent classes in our dataset, we were able to achieve an overall classification accuracy of 64% and a one-off accuracy of 96% on our holdout test set. Our model outperformed DenseNet121 in most metrics, and provided comparable performance to a vision transformer (ViT) while having fewer parameters overall, less complexity (useful for edge implementations), and some interpretability. Furthermore, we were able to show that our model outperformed expert human annotators by predicting more consistent and accurate wilt levels when considering single-image re-annotation. The results show that our proposed methodology can be a useful approach in detecting drought stress in soybean fields to facilitate efficient crop management and aid selection of drought-resilient varieties.

Plant phenotyping relevance

画像からダイズ葉の萎凋・干ばつストレスを推定するMIL手法の開発と性能比較が中心であり、植物状態の表現型推定に該当する。

Code and data availability

The paper's soybean leaf-wilt image dataset (1788 field images with expert wilt scores) is openly available on Zenodo, and the authors' MIL classification/analysis code is publicly available on GitHub, both explicitly stated in the Data Availability Statement.