Image-based wheat grain classification using convolutional neural network

India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of...

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Veröffentlicht in:	Multimedia tools and applications Jg. 80; H. 28-29; S. 35441 - 35465
Hauptverfasser:	Lingwal, Surabhi, Bhatia, Komal Kumar, Tomer, Manjeet Singh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2021 Springer Nature B.V
Schlagworte:	1166: Advances of machine learning in data analytics and visual information Processing Accuracy Agriculture Artificial neural networks Classification Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Gardening Grain Hand tools Image classification Machine learning Multimedia Information Systems Neural networks Plant diseases Special Purpose and Application-Based Systems Training Wheat Deep learning Image processing Wheat crops Convolutional neural network Classification
ISSN:	1380-7501, 1573-7721
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Abstract	India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of the country, it becomes a vital need for the customers as well as for the cultivators to identify and classify the grains based upon specific end products, demand, and prices of individual variety. The growth of Machine Learning and Computer Vision in agriculture, facilitate the development of such techniques that can successfully identify the classes based on visual features and representation. In this paper, a model has been developed from scratch for the classification of fifteen different varieties of wheat consists of 15000 images based on their visual traits using Convolutional Neural Network. The model has been produced under a different set of hyper-parameters tuned to develop the best model that can classify the varieties of wheat grains with high accuracy and minimum loss. The performance of the different models are compared in terms of classification accuracy and categorical cross-entropy loss. The model which is found best, successfully classifies the wheat varieties with 94.88% training accuracy and 97.53% test accuracy while on the other side reduces loss to 15% for training and 8% for the test set. Hence, the developed model can be deployed for the classification of different grain varieties, plant diseases, plant varieties, and several other fields under agriculture.
AbstractList	India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat to fulfill the specific requirements of food industries. Moreover, with the variations in prices of distinct varieties in different parts of the country, it becomes a vital need for the customers as well as for the cultivators to identify and classify the grains based upon specific end products, demand, and prices of individual variety. The growth of Machine Learning and Computer Vision in agriculture, facilitate the development of such techniques that can successfully identify the classes based on visual features and representation. In this paper, a model has been developed from scratch for the classification of fifteen different varieties of wheat consists of 15000 images based on their visual traits using Convolutional Neural Network. The model has been produced under a different set of hyper-parameters tuned to develop the best model that can classify the varieties of wheat grains with high accuracy and minimum loss. The performance of the different models are compared in terms of classification accuracy and categorical cross-entropy loss. The model which is found best, successfully classifies the wheat varieties with 94.88% training accuracy and 97.53% test accuracy while on the other side reduces loss to 15% for training and 8% for the test set. Hence, the developed model can be deployed for the classification of different grain varieties, plant diseases, plant varieties, and several other fields under agriculture.
Author	Tomer, Manjeet Singh Lingwal, Surabhi Bhatia, Komal Kumar
Author_xml	– sequence: 1 givenname: Surabhi surname: Lingwal fullname: Lingwal, Surabhi email: surabhi.lingwal@gmail.com organization: Govind Ballabh Pant Institute of Engineering and Technology – sequence: 2 givenname: Komal Kumar surname: Bhatia fullname: Bhatia, Komal Kumar organization: J. C. Bose University of Science and Technology, YMCA – sequence: 3 givenname: Manjeet Singh surname: Tomer fullname: Tomer, Manjeet Singh organization: J. C. Bose University of Science and Technology, YMCA
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Cites_doi	10.1007/s12393-014-9101-z 10.1002/jsfa.8080 10.1016/j.isprsjprs.2017.11.021 10.1038/nature14539 10.1007/s00371-017-1379-4 10.1007/s00371-018-1566-y 10.1007/s11632-013-0414-4 10.1109/ACCESS.2014.2325029 10.1007/s00371-018-1583-x 10.1109/TPAMI.2008.79 10.1109/TSMC.1979.4310076 10.1109/MCI.2010.938364 10.1016/j.compag.2018.08.013 10.1016/j.compag.2016.07.020 10.1016/j.patcog.2013.06.012 10.1007/s00371-013-0782-8 10.1016/j.eswa.2014.10.003 10.1002/jsfa.8264 10.1016/j.compag.2018.08.001 10.1080/10942912.2011.615085 10.1007/s00371-019-01763-x 10.1109/ICMLA.2016.0178 10.1145/3139367.3139368 10.1007/978-3-642-35289-8_3 10.1007/s00371-019-01768-6 10.1145/3209914.3209945 10.1007/978-981-13-1702-6_32 10.1007/978-3-319-90403-0_6 10.1109/Agro-Geoinformatics.2014.6910610
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Snippet	India is among the largest cultivators and consumers of wheat grains leading to apparent demand for identifying the quality and varietal distribution of wheat...
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SubjectTerms	1166: Advances of machine learning in data analytics and visual information Processing Accuracy Agriculture Artificial neural networks Classification Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Gardening Grain Hand tools Image classification Machine learning Multimedia Information Systems Neural networks Plant diseases Special Purpose and Application-Based Systems Training Wheat
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Title	Image-based wheat grain classification using convolutional neural network
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