使用超光谱成像和深度学习检测田间玉米中的尼科硫残留物的绿色和高效方法
Tianpu Xiao1, Li Yang1, Xiantao He1
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
|December 5, 2024
概括
一个新的深度学习模型,HerbiResNet,使用光谱数据准确检测玉米中的尼科硫除草剂残留物. 这项技术为精准农业和可持续农业实践提供了更快,更具成本效益的解决方案.
科学领域:
- 农业科学 农业科学
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 在玉米中检测尼科硫除草剂残留物对于有效的作物管理和安全至关重要.
- 目前的检测方法往往是缓慢的,昂贵的,劳动密集型,妨碍及时干预.
研究的目的:
- 开发和验证一种新的深度学习模型 (HerbiResNet) 以快速准确地检测玉米中的尼科苏尔除草剂残留物.
- 评估模型的性能与传统方法相比,并探索光谱数据和除草剂存在之间的相关性.
主要方法:
- 开发HerbiResNet模型,利用各种品种和除草剂度的玉米叶的光谱数据.
- 对低,中,高分类的残留量进行分析.
- 与支持向量回归 (SVR),部分最小平方回归 (PLSR),多层感知器 (MLP) 和AlexNet模型进行HerbiResNet性能比较.
主要成果:
- HerbiResNet实现了0.88的残留预测确定系数 (R2) 和0.87的残留水平分类准确度.
- 该模型显著优于传统的回归和经典的神经网络模型.
- 确定了特定光谱带 (550 nm,680 nm,750 nm,1000 nm) 和除草剂存在/生理变化之间的强烈相关性.
结论:
- HerbiResNet模型提供了一种高度准确和高效的方法,用于检测玉米中的尼科苏尔农药残留物.
- 频谱技术与深度学习相结合,显示出对推进精准农业和可持续农业的重大前景.
- 这种方法为在农业监测中使用光谱传感的更广泛应用奠定了基础.
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