AI-driven internet of Agro-Things adaptive farm monitoring systems for future agricultural production and food systems in Africa

Abstract

Emerging technologies like Machine Learning (ML) and the Internet of Things (IoT) assist farmers in addressing challenges and maximizing limited agricultural resources. Data-driven farming requires integrating various tools across the production chain, along with a System of Systems (SoS) approach for scalability, adaptability, and sustainability. Essential technologies include Artificial Intelligence (AI), Blockchain, big data analytics, remote sensing, and the Internet of Agro-Things (IoATs). This paper presents novel techniques for improving agricultural productivity among African small-holder farmers using ML and IoATs. It shares experiences in developing digital agriculture platforms, climate-smart farming, and a business-oriented approach. Key technologies covered are: (a) IoT-based agricultural systems, and (b) AI/ML for increasing productivity. The paper showcases real-time ML-driven IoATs implementation for farm-level crop monitoring and yield prediction. The paper outlines recommendations, trends, and research directions in digital and data-driven agriculture in Tanzania. By leveraging ML and IoT, this paper offers innovative techniques to transform agriculture and empower African small-holder farmers. Integration of advanced technologies and sustainable farming approaches contributes to addressing food security, resource efficiency, and economic development.