机器学习作为帮助科特迪瓦可可农的战略工具
Stefano Ferraris1, Rosa Meo2, Stefano Pinardi3
1Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin, 10125 Turin, Italy.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
农业中的人工智能有助于小农和政策制定者利用低成本工具打击气候变化. 机器学习模型改善了作物健康检测,并预测了环境效益的储水异常.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 农业科学 农业科学
背景情况:
- 气候变化对农业构成重大威胁,影响作物产量和水资源供应.
- 由于成本限制,发展中国家在采用先进的农业技术方面面临着独特的挑战.
- 由人工智能 (AI) 驱动的智能农业为环境可持续性和经济发展提供了潜在的解决方案.
研究的目的:
- 探索人工智能和机器学习在农业中为社会利益的应用,重点关注发展中国家的成本效益解决方案.
- 开发和评估用于两个关键农业任务的机器学习模型:作物健康监测和水资源管理.
- 为了证明使用低成本和开源工具在智能农业中实施AI的可行性.
主要方法:
- 深度神经网络 (YOLOv5m) 用手机图像检测健康和受损的可可植物和果.
- 来自NASA的GRACE任务和ERA5 (科珀尼克斯气候变化服务) 的遥感数据被分析为水资源管理.
- 一个新的深度神经网络架构 (CIWA-net) 与类似于U-Net的结构被提出用于预测总储水异常.
主要成果:
- 从手机图像中,YOLOv5m模型成功地区分了健康和受损的可可植物和果.
- CIWA网络架构显示出预测总储水异常的潜力.
- 该研究证实了在农业中使用可访问的硬件,软件和开放数据用于人工智能的可行性.
结论:
- 人工智能和机器学习,特别是低成本工具,可以赋予小农和区域利益相关者实施有效的气候变化对策的能力.
- 开发的模型显示了改善农业种植实践和加强发展中国家水资源管理的前景.
- 这项研究突出了人工智能的潜力,为农业部门的环境可持续性和经济弹性做出贡献.
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