Rosa Meo

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.

The challenge of this century is without a doubt to counter global warming. Land management, agriculture, and forests are responsible for 23% of total greenhouse gases (GHGs). In developing countries, such as those in African territories, where economic capacities are sometimes small and socio-cultural and linguistic perceptions are different, this requires a transition that is just and respectful of local culture and language, while at the same time helping to create labor conditions that respect gender and minors. This article describes a socio-technical ecological transition in the cocoa chain production in Côte d’Ivoire, which is not only the world’s leading producer of cocoa beans (45%) but also one of the African countries most prone to deforestation. Linguistic and multicultural aspects come together in Côte d’Ivoire, where more than 70 local languages are spoken. Intelligent digital approaches, agroecological issues, new methods, and sociocultural questions are embedded in a context of collaboration and co-creation, a living lab approach, to ensures sharing and co-creation among NGOs, farmers, companies, and researchers. A framework of socio-technical transition is presented, and this research aims to not only achieve the goals of a just ecological transition and reduce carbon footprint and deforestations but also to create diverse labour conditions, gender respect and inclusion.