Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism

The agriculture sector plays a pivotal role in the growth of the global economy, but remains highly susceptible to prediction errors, particularly in disease identification. To address the limitations of existing approaches, this study proposes a deep learning-based framework for the classification of medicinal plant leaf diseases. "Medicinal plant leaf images are collected from the standard data source. These images undergo a pre-processing phase that includes filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE) to enhance visual quality. Subsequently, an adaptive thresholding mechanism is employed for precise leaf segmentation. For effective disease recognition, deep feature extraction is carried out using a customized Multi-Scale VGG16 architecture," capturing diverse features such as color, shape, and texture. These heterogeneous features are then subjected to a weighted fused feature selection process, where feature weights are optimized using a novel Hybridized Zebra with Krill Herd Optimization (HZKHO) algorithm. The optimized feature set is input to the disease classification stage, which employs an Attention-based Dilated Adaptive DenseNet (A-DADensenet) model to produce accurate classification results. The proposed model achieves an impressive classification accuracy of 90.69%, thereby demonstrating its effectiveness in accurately identifying diseased medicinal plant leaves. "The integration of deep learning with a hybrid optimization technique" significantly enhances the model's classification performance, proving its potential for real-world agricultural applications.