ITDS | SPMS

CASSAVA LEAF DISEASE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Project Description
Supervisor

Cassava is a vital crop in Africa, However, its production is threatened by leaf diseases such as mosaic disease, brown streak disease, and bacterial blight. Detecting these early and accurately, can help to reduce crop losses and ensure food security. This project presents a mobile application that integrates a deep learning model with the EfficientNetB3 architecture to classify cassava leaf diseases from images. The model was trained and validated on a dataset of annotated cassava leaf images, achieving a classification accuracy of 96.52%. The trained model was deployed into a Flutter-based mobile application designed to operate offline, making it accessible for farmers in low-connectivity areas. The application provides farmers with instant disease detection and simple recommendations, thereby supporting timely decision-making in crop management. The results demonstrate that AI-powered mobile solutions can serve as affordable, scalable tools to enhance disease management and improve agricultural productivity.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

To facilitate the achievement of accessible, efficient, and accurate mobile-based solution that early detects and classifies the prevalent diseases that affect the cassava plant leaves and thus enable better management of the crop and livelihood of the farmers.

2024/2025

Feb 12, 2026

FOSU-HENE SYLVESTER, WAHAB IBRAHIM, OSEI DANIEL, OPOKU CLINTON, BARNIE CHRISTOPHER ATTA

BSc Information Technology