Domain‐independent adaptive histogram‐based features for pomegranate fruit and leaf diseases classification

Prajwala, Mohanmuralidhar; Palaiahnakote, Shivakumara; Prajwal Kumar, Prabhuswamy; Maheshwarappa Gopinath, Shanubhog; Basavanna, Mahadevappa; P. Lopresti, Daniel

Abstract

Disease identification for fruits and leaves in the field of agriculture is important for estimating production, crop yield and earnings for farmers. In the specific case of pomegranates, this is challenging because of the wide range of possible diseases and their effects on the plant and the crop. This study presents an adaptive histogram-based method for solving this problem. Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks. The approach explores color spaces, namely, Red, Green, and Blue along with Gray. The histograms of color spaces and gray space are analyzed based on the notion that as the disease changes, the color also changes. The proximity between the histograms of gray images with individual color spaces is estimated to find the closeness of images. Since the gray image is the average of color spaces (R, G, and B), it can be considered a reference image. For estimating the distance between gray and color spaces, the proposed approach uses a Chi-Square distance measure. Further, the method uses an Artificial Neural Network for classification. The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases. The results show that the method outperforms existing techniques in terms of average classification rate.

Citation

Prajwala, M., Palaiahnakote, S., Prajwal Kumar, P., Maheshwarappa Gopinath, S., Basavanna, M., & P. Lopresti, D. (2024). Domain‐independent adaptive histogram‐based features for pomegranate fruit and leaf diseases classification. CAAI Transactions on Intelligence Technology, https://doi.org/10.1049/cit2.12390

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2024
• Online Publication Date: Nov 12, 2024
• Publication Date: Nov 12, 2024
• Deposit Date: Feb 20, 2024
• Publicly Available Date: Nov 12, 2024
• Journal: CAAI Transactions on Intelligence Technology
• Print ISSN: 2468-2322
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1049/cit2.12390
• Keywords: image analysis, pattern recognition