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Analysis and Implementation of Disease Detection in Leafs and Fruit Using Image Processing and Machine Learning

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Abstract

The purpose of this study is to examine how different image processing techniques may be used to identify leaf disease. To identify and classify plant leaf diseases, different algorithms may be used to digital image processing, which is a quick, reliable, and accurate approach. Classifiers and support vector machines for illness classification are among the strategies presented in this study effort that have been employed by several authors to identify disease. Our research primarily focuses on the evaluation of several methods for detecting leaf disease and also gives an overview of various image processing methods. Fruit disease is also discussed in this study as a potentially disastrous issue that has the potential to harm both the economy and the agricultural industry. Because of technological advances, advanced image processing algorithms have recently been created to help identify contaminated fruit that was previously detected by hand. There are two stages: the first is for training, and the second is a testing phase. Data on infected and uninfected fruit is collected during the training phase, and during the testing phase, it is determined whether or not the fruit has been infected, and if so, by which disease. Various methods currently in use to identify infected fruit are examined in this piece of research. Farmers benefit from the use of these methods since they assist to identify fruit disease in its earliest stages.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IES College of Technology, Bhopal, Madhya Pradesh, India

    Manmohan Singh

  2. Department of CSE, TIT College, Bhopal, India

    Shaheen Ayuub

  3. School of CSE, VIT Bhopal University, Bhopal, India

    Arpita Baronia & Dheresh Soni

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Singh, M., Ayuub, S., Baronia, A. et al. Analysis and Implementation of Disease Detection in Leafs and Fruit Using Image Processing and Machine Learning. SN COMPUT. SCI. 4, 627 (2023). https://doi.org/10.1007/s42979-023-02045-z

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