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您的检索式：作者名="Surendra Palaiah"
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| 1 | Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images显示文摘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 yield.The conventional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of categorization.This process is admittedly subjective and error-prone,which in turn can lead to incorrect actions being taken in stress management decisions.The 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 images.Thework has adopted the pre-trained VGG-16 CNN model for the automatic classification of stressed paddy crop images captured during the booting growth stage.The 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. | Basavaraj S.Anami Naveen N.Malvade Surendra Palaiah | 2020 | Artificial Intelligence in Agriculture2020，，1： | 2 |
| 2 | Classification of yield affecting biotic and abiotic paddy crop stresses using field images显示文摘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 yield.The conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of categorization.This process is admittedly subjective and error-prone,which in turn may lead to incorrect action in stress management decisions.The 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 features.The 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 features.Three 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 types.The average stress classification accuracies of 89.12%,84.44%and 76.34%have been achieved using the BPNN,SVM,and k-NN classifiers,respectively.The 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. | Basavaraj SAnami Naveen NMalvade Surendra Palaiah | 2020 | Information Processing in Agriculture2020，7，2： | 1 |
| 3 | Automated recognition and classification of adulteration levels from bulk paddy grain samples显示文摘Fraudulent labeling and adulteration are the major concerns in the global rice industry.Almost all the paddy varieties being sold in the market are prone to adulteration.It is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual observation.Currently,there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain samples.The 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 features.In 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 features.The maximum average adulteration level classification accuracy of 93.31%is obtained using the BPNN classification model trained with PCA-based reduced features.The 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. | Basavaraj S.Anami Naveen N.Malvade Surendra Palaiah | 2019 | Information Processing in Agriculture2019，6，1： | 1 |
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