A hybrid deep learning model with adaptive feature fusion for automated rice leaf disease detection and classification

Abstract

Many countries greatly rely on agriculture as a means of livelihood and economic growth. Even the most industrialized countries need food, medicine, clothing, and shelter produced by crops. Rice is one of the most significant and widely grown crops worldwide. Nonetheless, the severely impacted crops in rice production are those of bacteria, fungi, and viruses, which decrease yield and quality. Manual disease detection is hectic, challenging, and, in most cases, inaccurate. Recent advances in deep learning and computer vision have demonstrated significant potential to improve the detection and classification of diseases. This study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases. This method consists of five key stages: image preprocessing, segmentation, augmentation, multi-feature extraction via adaptive fusion, and classification. There are five rice leaf diseases to discuss and recognize: Blight, brown spot, sheath blight, tungro, and leaf blast. The first step is global contrast enhancement, which improves image quality. After that, the segmentation is performed using Otsu’s Thresholding to extract the leaf area. Then, the modified VGG16 and modified ResNet50 networks are used in parallel to extract features using a transfer-learning approach. The adaptive fusion technique combines these features to obtain a dominant, proper feature representation. Lastly, the classification is done using an adaptive fusion score technique. Experimental results show excellent performance, with class-wise Precision in the range of 95.5–100%, class-wise recall in the range of 97.4–100%, and overall test accuracy of 98.5%.