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

2025-07-03

AI Summary

Rice leaf diseases cause substantial yield losses, necessitating early and accurate identification. This study systematically compares two paradigms: Feature Analysis–based Detection Models (FADM) and Direct Image-Centered Detection Models (DICDM). FADM integrates multi-scale feature extraction, dimensionality reduction (e.g., PCA), feature selection (e.g., mRMR), and Extreme Learning Machine (ELM) classification, evaluated via 10-fold cross-validation; DICDM adopts an end-to-end image-input approach. Experimental results demonstrate that FADM achieves significantly higher classification accuracy and computational efficiency than DICDM across multiple rice disease classes. These findings validate the effectiveness of the “feature-driven” paradigm in resource-constrained agricultural settings. The work contributes a lightweight, interpretable, and empirically grounded framework for intelligent crop disease diagnosis, offering both methodological innovation and practical guidance for deploying AI in low-infrastructure farming environments.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 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 bacterial leaf blight, brown spot, leaf blast, leaf scald, Sheath blight rot, and healthy leaf, utilizing 10-fold Cross-Validation method. 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 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. 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 overall productivity and sustainability of rice farming.

Problem

• Compares FADM and DICDM for rice disease classification
• Evaluates Feature Extraction Algorithms in disease detection
• Improves rice farming productivity via ANN-based models

Innovation

• ANN-based image-processing for rice disease classification
• Comparative analysis of FADM and DICDM models
• Feature Extraction Algorithms enhance detection accuracy