This paper addresses the global challenge of food production losses caused by plant diseases, pests, and nitrogen stress, focusing on the specific context of Ethiopia where cereal crop yields face a significant annual decline of 20–30%, i.e. losses of 420000 tons per year. Traditional fertilization methods have proven imprecise and inefficient. To tackle this issue, the study proposes an artificial intelligence-based system for early detection, analysis, and treatment of nitrogen stress in cereal crops, particularly corn. The integrated system combines Android applications and drone technology. The system demonstrates robust performance metrics, achieving a mean average precision exceeding 60. The model is described as secure, user-friendly, and reliable, making it suitable for testing in diverse African scenarios.