TL;DR
A lightweight deep learning-based approach for automatic detection and classification of walnut leaf diseases using convolutional neural networks (CNNs) is proposed, demonstrating that the proposed CNN model significantly outperforms conventional machine learning algorithms and pre-trained deep learning models.
A fine-tuned InceptionV3 convolutional neural network to classify five groundnut leaf classes showed excellent performance, especially for minority classes like rosette and rust, which support its use in real-world disease diagnosis in agriculture.
Five state-of-the-art deep convolutional neural network architectures are evaluated on a large-scale, field-acquired mungbean dataset that captures real-world variability across environmental conditions and disease severity levels, distinguishing it from controlled laboratory studies.
This research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations to strengthen plant disease monitoring systems.
A deep learning-based solution to automate disease detection of groundnut leaf conditions that outperformed existing methods such as ResNet50, CNN with progressive resizing, LeafNet and LeafNet and maintained low training and validation loss throughout training.
Findings indicate that the lightweight MobileNet architecture can effectively support reliable and scalable faba bean disease detection under real-world agricultural conditions.
The experimental results demonstrate that AFS-PLDCNet achieved superior classification accuracy compared to existing single-backbone CNNs and is well-suited for real-time, field-level leaf disease classification and precision agriculture systems.
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