Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
A. Pushpa Athisaya Sakila Rani , N. Suresh Singh
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引用次数: 0

Abstract

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.
基于分层EMD阶段特征的决策树方法对水稻病虫害和营养缺乏症进行分类鉴定
病虫害、病害和营养缺乏是制约水稻产量的主要因素。因此,本文提出了一种鉴定病虫害和营养缺乏症的分类体系。该方法首先使用熵滤波对叶片图像进行预处理,然后进行叶片分割处理。然后在叶子图像上构建多层,通过多层提取特征。采用灰度共生矩阵(GLCM)算法和主成分分析(PCA)方法提取叶片图像的全局纹理特征。在每一层上构造一维信号序列,通过经验模态分解算法对其进行分解,并从中估计相位特征。使用决策树分类器对特征进行训练/分类,决策树分类器对害虫攻击、疾病发病率和营养缺乏类别进行分类。该方法的精密度、准确度、特异性、敏感性和f1评分分别为97%、97.88%、96.52%、96.7%和96.7%。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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