Development of a Real-Time Maize Leaf Disease Classification System Deployed on Web and Mobile-Based Applications
TL;DR
This research aims to create a system that can identify diseases in maize based on images of the leaves using three deep convolutional neural network models, namely MobileNetV2, InceptionV3, and ResNet50, selected to achieve this goal because of their prior ability.
Abstract
Agriculture remains at the core of human life, providing staple food and livelihood for millions worldwide. Among its different domains, food crops directly enter the human system, while cash crops are grown primarily for monetary gains. Maize, as one of the most extensively grown and consumed food crops, is of gigantic economic and nutritional value, particularly in West Africa. Unfortunately, maize plant diseases have adversely impacted farmer yields, resulting in decreased maize production. This research aims to create a system that can identify diseases in maize based on images of the leaves. Three deep convolutional neural network (DCNN) models, namely MobileNetV2, InceptionV3, and ResNet50, were selected to achieve this goal because of their prior ability. The transfer learning technique was adopted to develop new models for classifying maize disease using a hybrid maize leaf image dataset comprising 6,543 images from the University of Pretoria and Kaggle repositories. Furthermore, the dataset was split into 80% for training, 10% for validation, and 10% for testing and the three model were configured and trained. According to the evaluation results, MobileNetV2 was the best model for classifying maize leaf diseases, with a 95.29% classification accuracy. In comparison, InceptionV3 and ResNet-50 yielded accuracies of 92.18% and 74.48%, respectively. The MobileNetV2 was chosen for the dual deployment in both a web-based and a mobile application due to its exceptional performance metrics and its lightweight API. Evaluation of the deployed MobileNetV2 model on both the web and mobile applications showed that it achieved an average confidence rate of 91% on both platforms, with response times of 0.159 s and 0.163 s, and throughputs of 5.88 images/s and 6.28 images/s, respectively. This research offers a simple and intuitive tool for users and agricultural professionals to quickly detect maize leaf diseases and take necessary precautions to mitigate losses.