Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin, 10125 Turin, Italy.
Department of Computer Science, University of Turin, 10149 Turin, Italy.
Sensors (Basel). 2023 Sep 3;23(17):7632. doi: 10.3390/s23177632.
Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d'Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network.
机器学习可以用于社会公益。人工智能在智慧农业中的应用对环境有许多好处：它有助于小农户（在本地规模）和政策制定者及合作社（在区域规模）采取有效和协调的对策应对气候变化。本文讨论了人工智能在农业中的应用如何能够帮助降低成本，特别是在科特迪瓦等发展中国家，仅使用低成本或开源工具，从硬件到软件和开放数据。我们为两个任务开发了机器学习模型：第一个是改善农业种植，第二个是水资源管理。对于第一个任务，我们使用深度神经网络（YOLOv5m）仅使用手机图像来检测可可的健康植物和豆荚以及受损的植物和豆荚。结果证实可以很好地区分健康的和受损的。对于更大规模的行动，第二个任务提出了对来自美国宇航局 GRACE 任务和哥白尼气候变化服务的 ERA5 的远程传感器的分析。提出了一种具有 U-Net 架构的新的深度神经网络架构（CIWA-net），旨在预测总储水异常。将模型质量与常规卷积神经网络进行比较。
