Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study analyzed residue levels in six maize varieties—both resistant and sensitive types—under two herbicide concentrations, categorizing residues into low, medium, and high levels. We developed the HerbiResNet model to predict and classify herbicide residues in maize leaves using spectral data. The model achieved a coefficient of determination (R²) of 0.88 for residue prediction and an accuracy of 0.87 for residue level classification on the test set, significantly outperforming traditional regression models (SVR, PLSR) and classical neural networks (MLP, AlexNet). Additionally, we explored combining spectral technology with deep learning, revealing strong correlations between specific spectral bands (around 550 nm, 680 nm, 750 nm, and 1000 nm) and herbicide residues as well as physiological changes in maize. This provides a solid theoretical foundation for the broader application of spectral technology in agriculture. Overall, the HerbiResNet model demonstrates substantial potential for precision agriculture and sustainable agricultural practices.

中文翻译：

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一种利用高光谱成像和深度学习检测大田玉米中烟嘧磺隆残留的绿色高效方法


准确、快速地检测田间玉米中的烟嘧磺隆除草剂残留对于实施化学修复和优化喷洒策略至关重要。然而，目前的检测方法既昂贵又耗时。本研究分析了两种除草剂浓度下六个玉米品种（包括抗性和敏感类型）的残留水平，将残留量分为低、中和高水平。我们开发了 HerbiResNet 模型，以使用光谱数据预测和分类玉米叶片中的除草剂残留。该模型在测试集上的残留预测决定系数 （R²） 为 0.88，残基水平分类的准确率为 0.87，明显优于传统回归模型（SVR、PLSR）和经典神经网络（MLP、AlexNet）。此外，我们还探索了光谱技术与深度学习的结合，揭示了特定光谱波段（550 nm、680 nm、750 nm 和 1000 nm 附近）与除草剂残留以及玉米生理变化之间的强相关性。这为光谱技术在农业中的更广泛应用提供了坚实的理论基础。总体而言，HerbiResNet 模型展示了精准农业和可持续农业实践的巨大潜力。