AI-driven internet of Agro-Things adaptive farm monitoring systems for future agricultural production and food systems in Africa
2024
Barakabitze, A.A. | Jonathan, J. | Sanga, C.
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.
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