Tomato disease detection system

Abstract

Tomato cultivation is vital to Uganda's agricultural sector, contributing significantly to food security and economic growth. Tomato diseases are a serious threat to tomato production worldwide, causing economic losses and food insecurity. Early and accurate detection of these diseases is important for appropriate intervention and better product health. In this project, we present the development of a mobile application for the detection of tomato diseases aimed at six major disease which include early blight, late blight, bacterial spot, bacterial canker, septoria leaf spot, anthracnose. This application uses a convolutional neural network (CNN) trained on a complete set of images to classify tomato diseases. This model was developed using the Teachable Machine friendly platform and then converted to the TensorFlow Lite model for optimal deployment on Android devices. The developed app allows users to capture images directly or select them from the gallery. The captured images are analyzed by a pre-trained CNN model to provide real-time classification results. If a disease is detected, the application displays the name of the disease and specific symptoms, recommended fertilizers for treatment and possible treatment methods. This project demonstrates the potential of using CNN approaches for plant disease detection in mobile application settings. This application has the potential to empower farmers and agricultural officers with easy to use tools to identify early diseases, allowing them to act in time to improve crop health and yields.