Open AccessComparative Analysis of DIP Techniques to Detect Leaf Diseases in Tomato Plant
Author(s) -
Pushpalatha S. Nikkam,
Archandibewoor,
Ayesha Ihsan Qazi,
Leena I. Sakri,
Vijayendra Nargund,
Aishwarya Sajjanar,
Saloni Porwal,
Sujit Bhosale,
Payal Ghorpade
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1065/1/012038
Subject(s) - decision tree , classifier (uml) , agriculture , support vector machine , yield (engineering) , computer science , microbiology and biotechnology , mathematics , artificial intelligence , machine learning , biology , ecology , materials science , metallurgy
In India agriculture is the main source of income for generating the economy. Diseases in plants are a major unavoidable problem, and hence detecting the diseases is the necessity of the day in the domain of agriculture. The main diseases found in tomato plants are viral, fungus and bacterial diseases. The detection will help improve the quantity and quality of the products with an optimum yield. In this paper a comparative analysis is carried out for the algorithms Support Vector Machine, Convolution Neural Networks, Decision tree classifier, and k-Nearest Neighbour (k-NN) with the result of 97%,97%,90% and 80% respectively.