用卫星图像和沿海地区的深度学习技术进行土壤和作物相互作用分析,用于产量预测
S Mahalakshmi1, A Jose Anand2, Pachaivannan Partheeban3
1Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India.
Journal of environmental management
|March 26, 2025
概括
这项研究引入了一种新的深度学习方法,用于使用卫星图像和土壤数据准确预测作物产量. 人工智能模型显著提高了预测准确度,帮助精准农业,提高了食品生产率.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 传统的作物产量预测方法往往不准确,特别是在数据有限的地区.
- 高分辨率传感器和深度学习 (DL) 的进步提高了农业数据的质量.
- 现有的研究往往独立预测土壤肥力和作物产量.
研究的目的:
- 使用先进的数据分析技术开发精确准确的作物产量预测模型.
- 将土壤类型信息与标准化差异植被指数 (NDVI) 数据整合起来,以加强农业建议.
- 通过准确的预测,为农民改善灌过程和农学决策.
主要方法:
- 利用 Sentinel-2 卫星图像进行作物健康分析和土壤类型预测.
- 采用规范差异植被指数 (NDVI) 来评估作物健康状况,值>0.7表示健康状况良好,<0.3表示压力.
- 使用卷积神经网络 (CNN) 方法和优质图像分析开发了一种降雨-流水预测,以减少错误率低于5%.
主要成果:
- 拟议的方法达到了98.7%的平均准确性,明显优于传统方法 (85%-90%).
- 该模型提供了五天的空间和时间分辨率,使得及时干预成为可能.
- 人工智能在农业中的应用同步了卫星图像,用于精准农业,可能将粮食生产率提高20%.
结论:
- 开发的AI模型为作物产量预测提供了高度准确的解决方案,超越了传统方法.
- 整合卫星数据和DL可以提高农业决策,资源利用和整体粮食生产率.
- 这项研究强调了人工智能在农业中的潜力,以促进经济和社会进步.
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