Open AccessDetection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques
Author(s) -
Abdul-Wahab Sami Ibrahim,
Baidaa Abdul khaliq Atya
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19100
Subject(s) - c4.5 algorithm , machine learning , agriculture , artificial intelligence , support vector machine , computer science , random forest , agricultural productivity , productivity , yield (engineering) , deep learning , agricultural engineering , engineering , biology , naive bayes classifier , ecology , materials science , macroeconomics , metallurgy , economics
Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image processing technique is used to extract the feature from the leaf images. Further for the classification of diseases various machine learning algorithm like the random forest, J48 and support vector machine is used and the result is compared among different machine learning algorithm. After model evaluation, classification accuracy is verified using the n-fold cross-validation technique.