Deep Learning-Based Classification of Sorghum Pests for Early Detection

Cereals and legumes are staple foods across many African countries. Despite their nutritional and economic importance, these crops are frequently attacked by pests whose infestations can partially or completely destroy the plants. This project aims to develop an automated recognition system capable of identifying pests and diagnosing the damage they cause, with a particular focus on sorghum crops in West and Central Africa. The system is based on image classification techniques and machine learning models designed to detect damage patterns associated with specific pest species. By analyzing visual indicators characteristic of each pest, the model attempts to infer the responsible species from the observed symptoms. Our results show high accuracy and IoU scores, highlighting the feasibility of AI-driven decision-support tools for smallholder farmers. This work contributes to sustainable agriculture by enabling early diagnosis and targeted interventions.