Development of an Improved Capsule-Yolo Network for Automatic Tomato Plant Disease Early Detection and Diagnosis

Like many countries, Nigeria is naturally endowed with fertile agricultural soil that supports large-scale tomato production. However, the prevalence of disease causing pathogens poses a significant threat to tomato health, often leading to reduced yields and, in severe cases, the extinction of certain species. These diseases jeopardise both the quality and quantity of tomato harvests, contributing to food insecurity. Fortunately, tomato diseases can often be visually identified through distinct forms, appearances, or textures, typically first visible on leaves and fruits. This study presents an enhanced Capsule-YOLO network architecture designed to automatically segment overlapping and occluded tomato leaf images from complex backgrounds using the YOLO framework. It identifies disease symptoms with impressive performance metrics: 99.31% accuracy, 98.78% recall, and 99.09% precision, and a 98.93% F1-score representing improvements of 2.91%, 1.84%, 5.64%, and 4.12% over existing state-of-the-art methods. Additionally, a user-friendly interface was developed to allow farmers and users to upload images of affected tomato plants and detect early disease symptoms. The system also provides recommendations for appropriate diagnosis and treatment. The effectiveness of this approach promises significant benefits for the agricultural sector by enhancing crop yields and strengthening food security.