A hybrid deep learning pipeline for rice disease detection using GAN-based augmentation and Otsu thresholding

Abstract

Rice is the main staple food of Pakistan. The rising demand for rice, driven by population growth, is under threat by the bacterial diseases such as leaf blight, leaf blast, and brown spot. These diseases adversely affect rice production, reducing yield and threatening food security. Technological advancements, particularly in deep learning, offer opportunities to strengthen agricultural practices in countries like Pakistan. Early and accurate detection of these diseases is critical to ensuring food security and preventing potential shortages. In this research, we propose a novel pipeline for rice disease detection using a hybrid approach that integrates Generative Adversarial Networks (GANs) for data augmentation, Otsu thresholding for image preprocessing, and a deep learning model for classification. While existing models like Inception V3 and MobileNet V2 have shown promising results on training datasets, their performance diminishes on larger, more diverse datasets, due to overfitting and poor generalizations. Our proposed hybrid pipeline aims to address rice disease detection challenges by integrating (i) synthetic data generation using a Pix2Pix GAN to augment the dataset, (ii) a pre-processing stage employing Otsu thresholding on the augmented images to enhance features, and (iii) a final classification stage using an InceptionDenseNet model trained on the processed data. Our suggested approach achieved an accuracy of 86.98%, a significant improvement from the baseline accuracy of 74% obtained by the Inception DenseNet model on the original dataset. This enhancement demonstrates the effectiveness of the combined GAN-based augmentation and Otsu thresholding pre-processing.