Maize Yield and Disease Risk Prediction Using Climatic and Soil Data: A Machine Learning Approach for Lesotho

The main staple crop in Lesotho (a country in southern Africa) is maize, but its production is extremely susceptible to unpredictable rainfall, temperature fluctuations, deficiencies in soil moisture, and frequent foliar disease pressure. As a result, smallholder farming households have ongoing yield shortfalls and food insecurity. The design, implementation, and assessment of a web-based decision-support system that uses supervised machine learning to forecast maize production and categorize disease risk using soil and climate data are presented in this study. Eight environmental and agronomic predictors: rainfall, temperature, relative humidity, soil moisture, cultivated area, week number, year, and administrative district, were used to independently train two Ordinary Least Squares (OLS) linear regression models on historical agricultural and environmental data spanning 40 years. Because of its interpretability, computational efficiency, and adaptability for implementation in environments with limited resources, linear regression was chosen. The system supports both single-record and bulk CSV prediction workflows and was built using a Flask (Python) backend, a Bootstrap 5 responsive frontend, a SQLite database for permanent storage, and Chart.js for dashboard analytics. The illness risk model had an R² of 0.76 and an RMSE of 0.10 across three interpretable risk categories (low, medium, and high), but the yield prediction model obtained a coefficient of determination (R²) of 0.88 and an RMSE of 2.09. Functional testing verified dependable performance in dashboard, storage, reporting, and prediction processes. The findings show that in data-poor agricultural settings like Lesotho, interpretable machine learning models may provide affordable, easily accessible decision assistance for early disease risk awareness and maize production planning