Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-Based Model and Direct Imaging Model.

Abstract

Rice leaf diseases significantly reduce productivity and cause economic losses, highlighting the need for early detection to enable effective management and improve yields. This study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases. Despite the prevailing approach of directly inputting images of rice leaves into ANNs, there is a noticeable absence of thorough comparative analysis between the Feature Analysis Detection Model (FADM) and the Direct Image-Centric Detection Model (DICDM), specifically when it comes to evaluating the effectiveness of Feature Extraction Algorithms (FEAs). Hence, this research presents initial experiments on the Feature Analysis Detection Model, utilizing various image Feature Extraction Algorithms, Dimensionality Reduction Algorithms (DRAs), Feature Selection Algorithms (FSAs), and Extreme Learning Machine (ELM). The experiments are carried out on datasets encompassing 3829 original rice leaf images across six classes (bacterial leaf blight, brown spot, leaf blast, leaf scald, sheath blight rot, and healthy leaf). A Direct Image-Centric Detection Model is established without the utilization of any FEA, and the evaluation of classification performance relies on different metrics. Ultimately, an exhaustive contrast is performed between the achievements of the Feature Analysis Detection Model and the Direct Image-Centric Detection Model in classifying rice leaf diseases. The results reveal that the highest performance is attained using the Feature Analysis Detection Model. We have also applied Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretability of the model's predictions. The adoption of the proposed Feature Analysis Detection Model for detecting rice leaf diseases holds excellent potential for improving crop health, minimizing yield losses, and enhancing the overall productivity and sustainability of rice farming.

Plant phenotyping relevance

イネ葉の病害状態を画像から分類する手法を中心に、特徴抽出モデルと直接画像モデルを比較・評価しており、植物病害フェノタイピング手法の開発・検証に該当する。

Code and data availability

The paper's rice leaf disease classification experiments are built on a publicly available Kaggle image dataset (3829 rice leaf images across six classes), explicitly linked in the Data Availability Statement. Datasets produced during the study are only available upon request from the corresponding author.