http://jer.unilag.edu.ng/issue/feedJournal of Engineering Research2024-09-18T18:42:00+00:00The Editor-In-Chiefkaiyesimoju@unilag.edu.ngOpen Journal Systems<p>JER is a peer-reviewed Journal and is quarterly published with focus on basic and applied researches in engineering and its related disciplines. It publishes contributions on concepts, state of the art, all aspects of research, standards, implementations, running experiments, and industrial case studies as well as significant advances in basic and applied engineering, engineering technology and management. The Journal also encourages the submission of critical review articles covering the latest advances in engineering and related fields as well as scientific commentaries.</p>http://jer.unilag.edu.ng/article/view/2236Development of a Tomato Leaf Disease Detection System Using Convolutional Neural Networks2024-09-18T18:42:00+00:00S Owoeyeowoeyeso@funaab.edu.ngFolasade Durodoladurodolaf@funaab.edu.ngM Adekunleadekunlem@funaab.edu.ngA Oyelamioyelamia@funaab.edu.ngB Bisiriyubisiriyub@funaab.edu.ng<p><em>The study and early detection of plant diseases are important because they harm plants and essential products like food, clothing, furniture, and housing. Plant diseases such as late blight, early blight, and septoria leaf spot significantly reduce tomato crop yields, posing major challenges to sustainable agriculture and food security. This paper aims to develop a cloud-based software system to accurately diagnose and recommend diseases in tomato leaves from images, providing recommended treatments for the detected diseases. The system integrates three neural networks: two Convolutional Neural Networks for object detection and feature extraction, and a third neural network for classification. These networks were trained on images from the Plant Village dataset, with preprocessing to enhance image quality and annotation accuracy. The system was tested and verified, achieving a training accuracy of 98% and a loss of less than 7.6% on the test set. However, real-world testing indicated an accuracy of around 80% for non-test-set images. The developed system has the potential to significantly aid in the early detection and management of tomato plant diseases, improving crop yields and agricultural sustainability. The software is accessible via an Application Programming Interface developed using TypeScript, and a frontend web application serves as a human-machine interface.</em></p>2024-09-18T00:00:00+00:00Copyright (c)