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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI)
Subburaman, Govindarajan
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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A novel approach to enhance rice foliar disease detection: custom data generators, advanced augmentation, hybrid fine-tuning, and regularization techniques with DenseNet121 Subburaman, Govindarajan; Selvadurai, Mary Vennila
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp237-247

Abstract

Rice leaf diseases impact crop yield, leading to food shortages and economic losses. Early, automated detection is essential but often hindered by accuracy challenges. This study contributes to improving model robustness against diverse and adversarial inputs by proposing a custom data generator that applies Albumentation-based advanced augmentations, such as Gaussian blur, noise addition, brightness/contrast adjustments, and coarse dropout, to enhance model generalization. Five deep learning architectures—simple convolutional neural network (CNN), ResNet50, EfficientNetB0, Inception v3, and DenseNet121—were evaluated for classifying six categories: bacterial blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaf. A hybrid model approach is proposed, fine-tuning the DenseNet121 model by unfreezing its last 20 layers, which balances transfer learning benefits with domain-specific feature extraction. Regularization techniques, including L2 regularization and a reduced dropout rate, are incorporated to control overfitting. Additionally, a custom learning rate scheduler is proposed to promote stable training. DenseNet121 achieved the highest performance, with an accuracy of 98.41%, demonstrating the effectiveness of these advanced augmentation and tuning strategies in rice leaf disease classification.
Unified voting-based ensemble learning for rice leaf disease detection using improved pretrained models Subburaman, Govindarajan; Selvadurai, Mary Vennila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1646-1663

Abstract

As a staple food for a large portion of the global population, rice is particularly susceptible to leaf diseases that adversely affect its yield and overall quality. This study utilizes four pretrained convolutional neural network (CNN) models to construct a unified voting-based ensemble approach for rice leaf disease classification. The models include VGG16, DenseNet121, InceptionV3, and Xception. The dataset used in this study was collected from Kaggle and further enriched with images obtained from Google sources. It comprises a total of 4,000 images categorized into six classes: bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves. It was split into training (327 images/class), validation (140 images/class), and testing (200 images/class). Images were normalized to [0,1] and augmented through rotation, flipping, shifting, shear, zoom, brightness, and channel adjustments to improve generalization. Individually, the fine-tuned models achieved accuracies of 91.3% (VGG16), 95.6% (DenseNet121), 92.1% (InceptionV3), and 89.8% (Xception). The ensemble leveraged majority voting (93.6%), weighted voting (96.5%), and soft voting (97%), yielding an absolute gain of 1.4% over the best individual model and 4.8% over the average of all models. To our knowledge, this is the first ensemble combining these four architectures with unified voting for identifying diseases in rice leaves, delivering a scalable and computationally efficient solution suitable in advance diagnosis and timely execution in agricultural settings with limited resources.
Co-Authors Selvadurai, Mary Vennila