文章基本信息

标题：An effective feature extraction method for rice leaf disease classificatio
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作者：Muhammad Anwarul Azim ; Mohammad Khairul Islam ; Md.Marufur Rahman 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2021
卷号：19
期号：2
DOI：10.12928/telkomnika.v19i2.16488
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Our society is getting more and more technology dependent day by day. Nevertheless, agriculture is imperative for our survival. Rice is one of the primary food grains. It provides sustenance to almost fifty percent of the world population and promotes huge amount of employments. Hence, proper mitigation of rice plant diseases is of paramount importance. A model to detect three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut is proposed in this paper. Backgrounds of the images are removed by saturation threshold while disease affected areas are segmented using hue threshold. Distinctive features from color, shape, and texture domain are extracted from affected areas. These features can robustly describe local and global statistics of such images. Trying a couple of classification algorithms, extreme gradient boosting decision tree ensemble is incorporated in this model for its superior performance. Our model achieves 86.58% accuracy on rice leaf diseases dataset from UCI, which is higher than previous works on the same dataset. Class-wise accuracy of the model is also consistent among the classes.
关键词：classification;extreme gradient boosting;image processing;machine learning;plant disease detection;rice leaf diseases;XGBoost