IoT Smart-Based Agriculture System for Small-Holder Farmers in Zimbabwe

This paper presents a comprehensive review of IoT and AI applications in agriculture, with a focus on smallholder farming in Zimbabwe. It examines existing smart farming solutions in regions such as East Africa and India, highlighting their adaptability and limitations in the Zimbabwean context. The study further proposes a low-cost, solar-powered IoT-based smart agriculture system designed to address local challenges such as water scarcity, erratic rainfall, and limited internet access. The proposed system integrates soil moisture, temperature, and humidity sensors with a LoRaWAN gateway and a Flutter-based mobile application to enable real-time monitoring and automated irrigation. Machine learning models, including Random Forest and CNN, are proposed for crop disease prediction and resource optimization. The paper concludes with a discussion on the potential impact of the system on crop yield improvement, water conservation, and climate adaptation, along with recommendations for policy integration and scalability within Zimbabwe’s National Agriculture Policy Framework.