Analysis of Feature Extraction Methods for Predicting Plant Diseases


Authors : K. Sree Lekha; Chinthala Karthik; Jakka Manideep Reddy; Katkuri Manoj

Volume/Issue : Volume 7 - 2022, Issue 6 - June

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

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

DOI : https://doi.org/10.5281/zenodo.6839234

Plant diseases have a catastrophic impact on the food production industry. Plant diseases lead to reduced quality and quantity of the produce. It also leads to huge losses for the farmers as well. There are various types of plant diseases which affect different plants in different ways. Countries where these plant diseases were not identified at an early stage have been heavily affected in the past. Thus, a quick and automatic identification of these plant diseases is highly desired. Quick identification will help in the appropriate diagnosis and will help reduce any loss. Thus, the automatic identification and diagnosis of plant diseases are highly desired in the field of agricultural information. Thus, a quick and automatic identification of these plant diseases is highly desired. Quick identification will help in the appropriate diagnosis and will help reduce any loss. Many methods have been proposed for solving this task, where machine learning is becoming the preferred method due to the impressive performance. In this work, we study the use of GLCM features extraction technique followed by application of various machine learning techniques.

Keywords : Augumentation; Filtering; Segmentation; Feature Extraction; GLCM.

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