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

Abstract

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.

Plant phenotyping relevance

薬用植物葉の画像から病害を分類する画像ベースの表現型推定手法を開発しており、葉の前処理・分割・特徴抽出・分類モデルが研究の中心である。

Code and data availability

The paper's medicinal plant leaf disease classification uses a public Mendeley Data image dataset (3838 soursop leaf images, six classes), explicitly cited with URL and access date in the Data availability statement. No author code or trained model is deposited.