ItemConference Paper

Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution

Title
Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution
Creator
Poorani, S.; Singh, Komal; Priya Stella Mary, I.; Brinda, P.; Srimathi, S.; Jegajothi, B.
Description
This study addresses the growing challenges posed by plant diseases, particularly in the rice industry, which is vital for many communities. The research propose a robust framework that integrates Deep Convolutional Neural Networks (Deep CNN) with advanced preprocessing techniques to identify rice leaf diseases, including Brown Spot, Leaf Blast, Hispa, and healthy leaves. Our approach employs normalization to enhance convergence during training and data augmentation to improve model generalizability. Additionally, implement the Synthetic Minority Over-sampling Technique (SMOTE) to create synthetic samples for under-represented classes, addressing class imbalance within the dataset. Experimental results demonstrate the model's impressive accuracy, achieving 98.2% for Brown Spot, 97.5% for Leaf Blast, 94.3% for Hispa, and 96.8% for healthy leaves. Furthermore, our method outperforms established CNN architectures such as AlexNet, VGG16, and ResNet50, showcasing the effectiveness of sophisticated preprocessing in enhancing plant disease detection systems and supporting food security initiatives. 2025 IEEE.
Source
International Conference on Intelligent Systems and Computational Networks, ICISCN 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
advanced preprocessing techniques; class imbalance resolution; data augmentation; deep convolutional neural network (deep CNN); rice leaf disease detection; synthetic minority over-sampling technique (SMOTE)
Coverage
Poorani S., Kongu Engineering College, Department of Computer Technology, Perundurai, Erode, India; Singh K., Babasaheb Bhimrao Ambedkar University, Lucknow, India; Priya Stella Mary I., Christ University, Department of Computer Science, Yeshwanthpur Campus, Bangalore, India; Brinda P., Veltech Hightech Dr. Rangarajan Dr. Sakunthala Engineering College, Department of Computer Science and Engineering, Avadi, Chennai, India; Srimathi S., Saveetha School of Engineering, Simats, Department of Bio Technology, Chennai, India; Jegajothi B., Srs Tech Solutions, Chennai, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152924-6;
Format
online
Language
English
Type
Conference paper

Citation

Poorani, S.; Singh, Komal; Priya Stella Mary, I.; Brinda, P.; Srimathi, S.; Jegajothi, B., “Robust Rice Leaf Disease Detection using Advanced Preprocessing and Deep CNNs for Class Imbalance Resolution,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26048.

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