STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Image Processing & Pattern Recognition Progress Review Article

A Review of Automated Pomegranate Disease Detection and Classification Using Machine Learning

  1. Vidya Shejwal Department of Electronics and Telecommunication Engineering, S.K.N. Sinhgad College of Engineering, Korti, Pandharpur
  2. K.J. Karande Department of Electronics and Telecommunication Engineering, S.K.N. Sinhgad College of Engineering, Korti, Pandharpur
  3. Anjali C. Pise Department of Electronics and Telecommunication Engineering, S.K.N. Sinhgad College of Engineering, Korti, Pandharpur

Abstract

The abstract outlines a research study focused on developing an automated system for detecting and classifying diseases that affect pomegranate fruits. Pomegranates, like many other crops, are vulnerable to several types of diseases that appear as visible colored spots on the fruit’s surface. These visible symptoms, such as lesions or discoloration, can significantly impact the fruit’s quality, market value, and yield. Therefore, timely and accurate identification of such diseases is crucial, especially during the fruit’s growth and harvesting stages. The study aims to address this issue by designing a system capable of identifying the most common pomegranate diseases, including bacterial blight, Cercospora fruit spot, fruit rot, and Alternaria fruit spot. These diseases not only reduce the marketability of the produce but can also spread rapidly if not controlled. Traditional disease identification methods are often manual, time-consuming, and prone to human error. To overcome these limitations, the proposed approach leverages machine learning techniques to automate the process of disease detection and classification. The system uses image processing and a machine learning approach to analyze the affected areas on fruit images, extract relevant features, and classify the severity of the disease. The abstract also highlights some key challenges, such as variability in image conditions, overlapping disease symptoms, and the need for accurate training data. Overall, this study proposes a technological solution aimed at improving disease monitoring efficiency, ensuring early intervention, and reducing crop losses in pomegranate farming.

Keywords

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