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 efficient alternative for the automated classification of chili leaf disease.
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 low-quality images are poorly classified). Moreover, they are mostly limited to leaf-only analysis and do not have the validation on field data. To eliminate these problems, this paper introduces a developed hybrid model combining Vision Transformers, ConvNeXt, and EfficientNetV2 to extract features better. It uses the entropy-, ANOVA-, and mutual information-based feature selection to eliminate dimensionality and class relevance. Preprocessing of images enhances the capabilities to resist degraded input, and multi-part analysis of the plants can be used by real-life data that goes beyond leaves. The framework has lower computational cost, less overfitting, greater robustness to noise and complex backgrounds, greater F1-scores on imbalanced data and can be applied to mobile/IoT-based real-time disease diagnostic.
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 real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.