在玉米中早期检测尼科硫毒性和生理预测,使用多分支深度学习模型和超光谱成像
Tianpu Xiao1, Li Yang1, Dongxing Zhang1
1College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
Journal of hazardous materials
|May 30, 2024
概括
一个新的AI模型HerbiNet使用高光谱图像准确检测玉米中尼科硫除草剂的毒性. 这项创新有助于早期干预,保护作物产量和质量,同时保护田间环境.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 人工智能的人工智能
背景情况:
- 在玉米种植中滥用除草剂导致大量的产量和质量损失.
- 早期和有效地检测除草剂毒性对于作物保护和环境健康至关重要.
研究的目的:
- 开发一种准确有效的模型,用于早期检测玉米中尼科苏尔农药毒性.
- 评估模型在预测毒性水平,土壤植物分析发展 (SPAD) 和水含量方面的表现.
主要方法:
- 开发了HerbiNet模型,使用了用尼科苏尔治疗的玉米作物的高光谱图像.
- 对HerbiNet与支持向量机器,AlexNet和部分最小平方回归的验证.
- 开发了一种轻量级版本,HerbiNet-Lite,使用最小光谱波长.
主要成果:
- 在治疗后4天,HerbiNet在预测毒性方面达到91.37%的准确性,SPAD的R2值为0.82,含水量为0.73.
- 与其他模型相比,HerbiNet在不同数据集和季节中展示了优越的概括性.
- HerbiNet-Lite实现了87.93%的准确性,SPAD的R2值为0.80,含水量为0.71,同时最大限度地减少了过.
结论:
- 赫比网模型提供了一种创新和有效的方法,用于早期和准确地检测玉米中尼科硫的毒性.
- HerbiNet-Lite为实时毒性评估提供了一个计算效率高的替代方案.
- 这项技术支持可持续农业,通过及时干预来减轻除草剂损害.
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