A Review on Fruit Disease Detection and Classification using Computer Vision Based Approaches


Authors : S. Kavitha; Dr. K. Sarojini

Volume/Issue : Volume 7 - 2022, Issue 3 - March

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3kcqNZx

DOI : https://doi.org/10.5281/zenodo.6486739

Plant Diseases are one of the leading reasons of economic shortfalls in agricultural and farming sectors worldwide. It is the most essential element since it reduces crop quantity and quality significantly. Fruits are one of the largest essential nutritional resources from plants. Unfortunately, a variety of conditions might impair both the content and outcome of fruits. As a result, an autonomous Computer Vision (CV) -based approach for reliable Fruit Disease Detection (FDD) is necessary. CV is an Artificial Intelligence (AI) area that allows software and algorithms to extract relevant data from digital images. Over the past decades, advanced AI techniques such as Machine Learning (ML) and Deep Learning (DL) algorithms have been developed to predict and classify FDs early from different imaging modalities. The findings observed from these techniques can help farmers with FDD and treatment. This paper presents a detailed review of different ML and DL algorithms developed to predict and classify FDs from different fruit images. First, different FDD and classification systems designed by many researchers based on ML and DL algorithms are studied in brief. Then, a detailed analysis is carried out in order to identify the shortcomings of existing algorithms and to provide a novel strategy for properly classifying fruit pathogens.

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