Apple Plant Disease Detection System using Leaf Images
DOI:
https://doi.org/10.63503/j.ijcma.2026.218Keywords:
Apple Plant, Disease Detection, LetNet, AlexNet, VGG, Resnet, Inception Net, DenseNetAbstract
The cultivation of apples is affected by various apple plant diseases. These diseases, if not identified and treated on time, may lead to considerable losses in yield. Early detection is highly essential in order to provide early warnings to farmers and to help in identifying diseases at an early stage so that further action can be done to prevent the spread of disease as these diseases cannot be identified through naked eyes in their early stages. This leads to less wastage of yield. This paper proposes a comparison among the deep learning models such as LetNet, AlexNet, VGG, Resnet, Inception Net, and DensNet, for the efficient classification of leaf diseases of the apple plant. The model is trained on the Plant Village Dataset (Updated) taken from kaggle, which contains both healthy and diseased leaf images of apple plants. In this, images go through various preprocessing techniques like resizing, normalizing, and augmenting images in order to increase the robustness of the model. AlexNet attained the maximum classification accuracy in initial trials but had the largest number of parameters, didn't use modern regularization, and hence got at risk of overfitting at some early stage. The paper improved their performance by using a hybrid architecture which consisted of MobileNetV3 and ResNet50 because MobileNetV3 offered efficient extraction of features with little computational expense. It was further complemented by the depth features offered by ResNet50. The main aim for proceeding with the idea of hybrid architecture was not only to improve generalization but also to prevent overfitting.
The hybrid model is implemented using streamlit. This web interface allows the users to upload images of leaves and get real-time results predicting whether the leaves are affected by a disease or not. The system demonstrates high classification accuracy and effective differentiation among visually similar diseases. However, the model's performance in terms of empirical data analysis is influenced by dataset quality, computational resource demands, and its limited ability to generalize in the presence of sparse data. Despite these challenges, the proposed solution provides a scalable and accessible tool to assist farmers and agricultural experts in early disease detection and management.
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