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Classification of yield affecting biotic and abiotic paddy crop stresses using field images

Information Processing in Agriculture 2020年第2期7卷 272-285页

作者： Basavaraj SAnami Naveen NMalvade surendra palaiah Department of Computer Science and Engineering K.L.E.Institute of TechnologyHubballi 580030India Visvesvaraya Technological University Belagavi590018 KarnatakaIndia Department of Information Science and Engineering K.L.E.Institute of TechnologyHubballi 580030India Department of Genetics and Plant Breeding University of Agricultural SciencesDharwar580002 KarnatakaIndia

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural *** conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of *** process is admittedly subjective and error-prone,which in turn may lead to incorrect action in stress management *** proposed work presented in this paper aims to develop an automated computer vision system for the recognition and classification of paddy crop stress types from the field images using the state-of-the-art color *** work examines the impact of eleven stress types,two biotic and nine abiotic stresses,on five different paddy crop varieties during the booting growth stage using field images and analyzes the stress responses in terms of color variations using lower-order color moments and two visual color descriptors defined by the MPEG-7 standard,the Dominant Color Descriptor(DCD)and Color Layout Descriptor(CLD).The Sequential Forward Floating Selection(SFFS)algorithm has been employed to reduce the overlapping between the *** different classifiers,the Back Propagation Neural Network(BPNN),the Support Vector Machine(SVM),and the k-Nearest Neighbor(k-NN)have been deployed to distinguish among stress *** average stress classification accuracies of 89.12%,84.44%and 76.34%have been achieved using the BPNN,SVM,and k-NN classifiers,*** proposed work finds application in the development of decision support systems and mobile apps for the automation of crop and resource management practices in the field of agricultural science.

关键词： Paddy crop stress Color moments MPEG-7 color descriptors BPNN SVM k-NN

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Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

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Artificial Intelligence in Agriculture 2020年第1期4卷 12-20页

作者： Basavaraj S.Anami Naveen N.Malvade surendra palaiah Department of Computer Science and Engineering K.L.E.Institute of TechnologyHubballi 580030KarnatakaIndia Department of Information Science and Engineering K.L.E.Institute of TechnologyHubballi 580030KarnatakaIndia Department of Genetics and Plant Breeding University of Agricultural SciencesDharwar 580002KarnatakaIndia Visvesvaraya Technological University Belagavi 590018KarnatakaIndia

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural *** conventional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of *** process is admittedly subjective and error-prone,which in turn can lead to incorrect actions being taken in stress management *** work presented in this paper aims to design a deep convolutional neural network(DCNN)framework for automatic recognition and classification of various biotic and abiotic paddy crop stresses using the field *** has adopted the pre-trained VGG-16 CNN model for the automatic classification of stressed paddy crop images captured during the booting growth *** trained models achieve an average accuracy of 92.89%on the held-out dataset,demonstrating the technical feasibility of using the deep learning approach utilizing 30,000 field images of 5 different paddy crop varieties with 12 different stress categories(including healthy/normal).The proposed work finds applications in developing the decision support systems and mobile applications for automating the field crop and resource management practices.

关键词： Paddy crop Biotic and abiotic stress CNN VGG-16

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Automated recognition and classification of adulteration levels from bulk paddy grain samples

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Information Processing in Agriculture 2019年第1期6卷 47-60页

作者： Basavaraj S.Anami Naveen N.Malvade surendra palaiah Department of Computer Science and Engineering K L E Institute of TechnologyHubballi 580030India Department of Information Science and Engineering K L E Institute of TechnologyHubballi 580030India Department of Genetics and Plant Breeding University of Agricultural SciencesDharwar580002India

Fraudulent labeling and adulteration are the major concerns in the global rice *** all the paddy varieties being sold in the market are prone to *** is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual ***,there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain *** paper presents a cost-effective image processing technique for the recognition of adulteration and classification of adulteration levels(%)from the images of adulterated bulk paddy samples using state-of-the-art color and texture *** this work,seven adulterated bulk paddy samples are considered and each of the samples is prepared by mixing a premium paddy variety with the identical looking and commercially inferior paddy variety at five different adulteration levels(weight ratios)of 10%,15%,20%,25%and 30%.The study compares the performances of three different classification models,namely,Multilayer Back Propagation Neural Network(BPNN),Support Vector Machine(SVM)and k-Nearest Neighbor(k-NN).The Principal Component Analysis(PCA)and Sequential Forward Floating Selection(SFFS)methods have been employed separately for the automatic selection of optimal feature subsets from the combined color and texture *** maximum average adulteration level classification accuracy of 93.31%is obtained using the BPNN classification model trained with PCA-based reduced *** proposed technique can be used as an economic,rapid,non-destructive and quantitative technique for testing adulteration,authenticity,and quality of bulk paddy grain samples.

关键词： Bulk paddy grains Adulteration quantification Feature selection GLCM LBP BPNN PCA SVM k-NN

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