Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model
Journal:
arXiv
Published Date:
Jul 3, 2025
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.
