Banana Leaf Disease Detection With Multi Feature Extraction Techniques Using SVM

Evuri, Sai Rajasekhar Reddy (2022) Banana Leaf Disease Detection With Multi Feature Extraction Techniques Using SVM. Masters thesis, Dublin, National College of Ireland.

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Abstract

Farming is how the majority of people in developing countries make a living. As a result of climate change, farmers face numerous challenges. Plants that are farmed are susceptible to various diseases. Not many farmers are able to obtain the expert opinion of an eyewitness. The sickness must be detected as soon as possible using the simplest method. A method of detecting these diseases can be implemented by leveraging the technology of machine learning. This article describes an automated method that uses color, shape, and texture information to detect illnesses in banana plants. Data classification methods include Support Vector Machine (SVM) Classification Techniques. The proposed study displayed an average accuracy of 90 Percentage to distinguish between two types of diseases namely Sigatoka and Xanthomonas by combining GLCM and NGTDM features.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QK Botany S Agriculture > SB Plant culture Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	24 Jan 2023 14:46
Last Modified:	03 Mar 2023 12:30
URI:	https://norma.ncirl.ie/id/eprint/6117

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