A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.
A hybrid framework for the classification of chili leaf disease that combines DenseNet201-based deep feature extraction with a Broad Learning System (BLS) classifier is investigated, indicating that the integration of DenseNet201 feature extraction with a Broad Learning System offers a competitive and computationally e...
The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications and effectively integrates global semantic information with local disease-specific features for improved classification performance.
Existing classification systems of hybrid plant disease detection use CNN-based features extraction (e.g., VGG-16, ResNet-34, EfficientNet-B4) with SVM classification but are subject to major limitations (redundant features are extracted, features and classes are not well correlated, small datasets are overfitted, and...
An automatic disease detection and classification framework using a Deep Convolutional Recurrent Neural Network (DCRNN) enabled by the optimization process of an Enhanced Sea Horse Optimization (ESHO) algorithm is developed.
Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under...
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