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
Abstract :
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.
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.
