Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorus-deficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p < 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops.

Maize (Zea Mays) is one of the world's most important staple crops, providing food for humans and feed for livestock. However, its production is threatened by a range of stresses, including crop diseases, which significantly reduce yields, particularly in smallholder farming systems. Traditional disease detection methods, such as visual inspection, are often labour-intensive, subjective, and prone to error, leading to delayed interventions and widespread crop losses. This study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases in a smallholder farm located in the Mopani District of Limpopo Province, South Africa. UAV-derived vegetation indices including NDVI, GNDVI, and NDRE were combined with UAV multispectral bands and the three ML algorithms, namely - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), to first distinguish healthy from diseased plants and then to classify specific maize diseases. The SVM algorithm achieved the highest accuracy in both, distinguishing healthy and diseased crops from other land cover classes (91.73%) and in distinguishing specific diseases (89.41%). Among the diseases identified, Southern Corn Leaf Blight was classified with the highest user's accuracy, while phosphorus deficiency had the lowest user's classification accuracy. The results demonstrate the potential of integrating UAV-based multispectral imaging and ML for precision agriculture by providing timely, spatially detailed disease information that enables targeted management practices, reducing crop losses and enhancing food security for smallholder farmers.