设计和实施深度学习框架,用于精准农业的自动作物分类和健康诊断
Atul Kumar Pal1, B D K Patro1, Shshank Chaube2
1Department of Computer Science and Engineering, Rajkiya Engineering College, Kannauj Affiliated with Abdul Kalam Technical University (AKTU), Jankipuram Vistar, Lucknow, Uttar Pradesh, 226031, India.
Scientific reports
|March 1, 2026
概括
一个新的深度学习框架使用无人机和卫星数据进行实时作物健康分类. 这种自动化系统达到90%以上的准确性,提高了精准农业.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 手动作物健康诊断是劳动密集型和主观的.
- 整合多模式数据源可以提高诊断准确性.
- 现有的方法往往缺乏实时能力,以便及时干预农民.
研究的目的:
- 开发和验证用于自动化实时作物健康分类的深度学习框架.
- 将无人机和卫星的多模式数据融合在一起,以提高诊断可靠性.
- 减少对手工评估的依赖,改善农民的决策.
主要方法:
- 一个三阶段的深度学习框架:多模式数据采集 (无人机,卫星,物联网),标准化预处理 (插值) 和基于CNN的特征提取.
- 利用基于神经网络的数学模型对作物条件进行分类和检测.
- 验证了玉米,土豆和小麦数据集的框架,使用70%的培训,15%的验证,15%的测试分割.
主要成果:
- 该框架在对玉米,土豆和小麦等主食作物的健康状况进行分类时,达到90%以上的准确性.
- 证明了宏观卫星图像与微观无人机和传感器数据的成功融合.
- 该模型有效地学习了用于有意义的作物健康诊断的特征.
结论:
- 开发的深度学习框架为实时作物健康监测提供了可靠的,自动化的解决方案.
- 实施可以显著降低劳动力成本,提高作物生产率,并支持可持续的农业实践.
- 这种方法通过为农民提供及时,高质量的信息来改善决策,从而推进精准农业.
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