文章基本信息

标题：Automated Disease Detection and Classification from Plant Leaf Images
本地全文：下载
作者：S.Iniyan ; R.Jebakumar
期刊名称：Ilköğretim Online/Elementary Education Online
印刷版ISSN：1305-3515
电子版ISSN：1305-3515
出版年度：2021
卷号：20
期号：1
页码：3067-3076
DOI：10.17051/ilkonline.2021.01.344
语种：English
出版社：Öğretmen Eğitimi Akademisi
摘要：India’s economy is vastly based on agriculture. Increase in the productivity of agricultural fields affects the GDP of the nation. Early and automated crop disease detection is an important step to increase the productivity of these farms. This paper proposes a model using machine learning classifiers for the detection and classification plant disease and is based on tomato plant but similar results can also be obtained using other crops. Experimental results also reveal that 76 % and 72% of accuracy level obtained using Extremely Randomized Tree classifiers and Random forest outperformed other machine learning classifiers. Though our model not produced higher accuracy when compared to other pre-trained Convolutional Neural Network (CNN) models such as Res net and Image Net, the difference in time complexity made our model less memory extensive..
关键词：Agriculture;Disease Identification;Machine Learning;Image Processing;Random Forest;KNearest Neighbours