Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.

Abstract

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Plant phenotyping relevance

冬小麦の干ばつストレスという植物状態を、画像・土壌水分・気象センサーを統合した深層学習モデルで非破壊推定する手法が研究の中心であり、技術性能も比較評価している。

Code and data availability

The authors deposited the minimal multimodal deep learning dataset (winter wheat drought stress images with soil moisture/meteorological data) in a public Kaggle repository, explicitly stated in the Data Availability section.