Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Abstract

Mango leaf diseases represent a serious threat to world agriculture, necessitating prompt and accurate detection to avert catastrophic effects. In response, this study suggests a light-weight, deep learning-based method for automatically classifying mango leaf diseases. The model is based on the original DenseNet architecture, which is well known for its effectiveness in image classification tasks. Custom layers have been added over the existing layer of the original DenseNet model. The proposed model has been compared with other existing pre-trained models. Based on comparisons, the proposed model, DenseNet78, proved to be efficient even on a relatively small dataset, where the conventional model failed. The proposed model ensured generalization across regions, disease variants, and diverse datasets of mango leaves. The results demonstrate that the fine-tuned DenseNet architecture (DenseNet78), along with an ideal growth rate, modifying block size, and a number of layers, provides optimum accuracy, with 99.47% accuracy in identifying healthy mango leaves and 99.44% accuracy in detecting various mango leaf diseases. The results also demonstrate that the model is effective in accelerating the training process because of careful comparative analysis of all the available alternatives, including the most effective combination of optimizers, learning rate schedulers, and loss functions. The study's conclusion is an automated approach for diagnosing mango leaf disease using an improved and optimized DenseNet architecture (DenseNet78). Doi: 10.28991/ESJ-2024-08-01-03 Full Text: PDF