Pre-Symptomatic Detection of Nicosulfuron Phytotoxicity in Vegetable Soybeans via Hyperspectral Imaging and ResNet-18

文献类型： 外文期刊

第一作者： Xiang, Yun

作者： Xiang, Yun;Liang, Tian;Cai, Shiqiang;Su, Zhongjing;Hu, Jinxuan;Cai, Chang;Xiang, Yun;Liang, Tian;Cai, Shiqiang;Su, Zhongjing;Hu, Jinxuan;Cai, Chang;Bu, Yuanpeng;Wang, Bin;Feng, Zhijuan;Zhang, Guwen;Liu, Na;Gong, Yaming;Guo, Jingjie

作者机构：

关键词： spectral range; herbicide phytotoxicity; early stress detection; deep learning; soybean-corn intercropping

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 7 期

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收录情况： SCI

摘要： Herbicide phytotoxicity represented a critical constraint on crop safety in soybean-corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386-1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518-690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation.

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