Enhancing Apple Leaf Disease Detection: A CNN-based Model Integrated with Image Segmentation Techniques for Precision Agriculture

Abstract

The agriculture industry has an enormous influence on a nation's economy. Loss of yield due to plant diseases remains a reason, reducing crop quantity and quality. Incorrect diagnosis of crop diseases can result in improper application of chemical pesticides, which promotes immune microbial strains, raises expenses, and triggers fresh outbreaks that are harmful to the economy and the ecosystem. Despite the potential of Machine Learning (ML) and Deep Learning (DL) approaches in plant disease detection, their limited effectiveness results in poor or late disease detection. Resolving this issue is critical, requiring the development of more accurate disease detection methods. This research introduces an innovative approach for the detection of apple leaf diseases utilizing the CNN-based Inception-v3 model. The dataset comprises images taken on location without having any control over the image-capturing settings may provide better relevance to real-world scenarios. The proposed method integrates canny edge detection and watershed transformation to achieve accurate image segmentation, thereby enhancing the identification of disease regions. Additionally, exploratory data analysis was performed, and channel distributions were visualized to understand the dataset's characteristics. To ensure robust evaluation, the model's performance underwent stratified 5-fold cross-validation. The model classified plant images with 84.60% precision, 87.40% recall, 85.00% F1-score, and 94.76% accuracy. Experimental results substantiate the efficacy of the proposed approach, surpassing existing methods in disease classification.