基于深度学习的非生物作物压力评估准确农业:全面审查
1Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, HP, 177005, India; Agricultural Mechanization Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, 462038, MP, India.
Journal of environmental management
|April 9, 2025
概括
深度学习模型使用各种数据快速识别作物非生物压力,如水,营养和温度问题. 这项技术是提高作物产量和确保全球粮食安全的关键.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 无生物应激显著降低作物产量,威胁全球粮食安全.
- 早期和准确地检测作物压力对于有效管理至关重要.
- 深度学习 (DL) 显示了高通量作物压力表型化的潜力.
研究的目的:
- 对非生物作物压力评估的深度学习应用进行审查.
- 在各种数据模式 (物联网,热,光谱,RGB,图像) 中分析DL模型 (ANN,CNN,RNN,ViT).
- 讨论压力分类,局部化和量化方面的DL.
主要方法:
- 对非生物压力深度学习模型的综合文献综述.
- 对各种数据源的分析:物联网传感器,热,光谱,RGB,现场,无人机和卫星图像.
- 对复杂作物压力数据的DL原则和能力的检查.
主要成果:
- 深度学习模型有效地评估非生物压力,包括水,营养素,盐度,温度和重金属.
- DL方法应用于应力分类,局部化和量化.
- 各种DL架构在多种数据模式中展示了实用性.
结论:
- 深度学习是推进非生物作物压力评估的强大工具.
- DL能够实现数据驱动的精准农业,以提高作物管理.
- 挑战包括有限的标记数据,模型可解释性和互操作性.
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