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.
