Less Is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification
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Product Description
Aim:
To detect the plant leaf diseases using neural network for high accuracy detection
Synopsis:
To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally in expensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pre-trained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the Plant Village dataset shows that the proposed architecture achieves high accuracy and operations, making it a suitable choice for low-end devices.
Proposed System:
Our proposed architecture takes tomato leaf images as input and outputs the class labels. At first, the input image is passed through a preprocessing step where it is enhanced using Adaptive Histogram Equalization. Then, the image is fed to a transfer learning block, where we utilize a pre-trained deep CNN model for efficient feature extraction. To determine a suitable feature extractor, we ex-perimented with pre-trained architectures which are MobileNet. Based on the results, we have chosen MobileNet due to its smaller size and faster inference while maintaining com-parable accuracy. Then the features extracted by the pre-trained model are fed through a shallow densely connected classifier network to
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