Machine Learning-Based Pomegranate Disease Detection and Treatment

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Author(s): Kutubuddin Sayyad Liyakat Kazi (Brahmdevdada Mane Institute of Technology, Solapur, India)
Copyright: 2024
Pages: 30
Source title: Revolutionizing Pest Management for Sustainable Agriculture
Source Author(s)/Editor(s): Muhammad Zia Ul Haq (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan)and Iftikhar Ali (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan)
DOI: 10.4018/979-8-3693-3061-6.ch019

Keywords: Agricultural and Food Sciences / Engineering Science Reference / Environment & Agriculture / Sustainable Development

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Abstract

There are two components to the assignment of pomegranate disease detection. A neural network model is created in the first part of the process, which includes image acquisition, labelling, segmentation, resizing, image stacking, and label stacking. The model is then trained using database images. The second component of the framework uses the developed model to predict image classification. The framework is implemented using a pomegranate leaf disease image dataset that is split into training and test sets. The suggested approach to solving the issue depends on creating machine learning-ml algorithm and ANN building blocks during the implementation phase.

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Machine Learning-Based Pomegranate Disease Detection and Treatment

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