AI-Driven Decision Support System for Optimizing Soil Analysis and Crop Management in Zimbabwe: A Focus on Maize and Wheat

Abstract

Smallholder farmers in Zimbabwe face challenges such as limited access to data-driven tools, resource constraints, and climate variability, necessitating a solution to bridge traditional farming methods with modern agricultural technologies. This study aimed to develop an AIDriven Decision Support System (AI-DSS), named Ivhumunhu, to optimize soil analysis and crop management practices for these farmers. The AI-DSS was developed by integrating soil sensor data, satellite imagery, and weather forecasts with machine learning algorithms, including Random Forest for soil nutrient classification and Long Short-Term Memory (LSTM) networks for yield forecasting, to provide personalized recommendations on fertilizer application, irrigation, and crop rotation. Field trials in Zimbabwe's Makonde District demonstrated that Ivhumunhu improved soil fertility by increasing nitrogen levels by 25%, enhanced water usage efficiency by reducing irrigation needs by 18%, and increased maize and wheat yields by 15% over a single growing season, with the system achieving a 92% accuracy rate in diagnosing soil health conditions. The successful implementation of Ivhumunhu demonstrates the potential of AI-driven technologies to transform agriculture in resourceconstrained settings, providing a scalable model for other regions.