{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:00:37Z","timestamp":1765886437538,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981809"},{"type":"electronic","value":"9789819981816"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_43","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"565-576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Classification of\u00a0Hard and\u00a0Soft Wheat Species Using Hyperspectral Imaging and\u00a0Machine Learning Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3008-8399","authenticated-orcid":false,"given":"Nitin","family":"Tyagi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6277-6267","authenticated-orcid":false,"given":"Balasubramanian","family":"Raman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2877-816X","authenticated-orcid":false,"given":"Neerja","family":"Garg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"issue":"4","key":"43_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1089\/big.2018.0175","volume":"7","author":"HA Abu Alfeilat","year":"2019","unstructured":"Abu Alfeilat, H.A., et al.: Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big Data 7(4), 221\u2013248 (2019)","journal-title":"Big Data"},{"key":"43_CR2","unstructured":"Allahverdiyev, T.I., Talai, J.M., Huseynova, I.M., Aliyev, J.A.: Effect of drought stress on some physiological parameters, yield, yield components of durum (Triticum durum desf.) and bread (Triticum aestivum L.) wheat genotypes. Ekin J. Crop Breed. Genet. 1(1), 50\u201362 (2015)"},{"issue":"19","key":"43_CR3","doi-asserted-by":"publisher","first-page":"4119","DOI":"10.3390\/app9194119","volume":"9","author":"Y Bao","year":"2019","unstructured":"Bao, Y., Mi, C., Wu, N., Liu, F., He, Y.: Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics. Appl. Sci. 9(19), 4119 (2019)","journal-title":"Appl. Sci."},{"key":"43_CR4","doi-asserted-by":"crossref","unstructured":"Berrar, D.: Bayes\u2019 theorem and Naive Bayes classifier. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, vol. 403, p. 412 (2018)","DOI":"10.1016\/B978-0-12-809633-8.20473-1"},{"issue":"2","key":"43_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.biosystemseng.2008.09.028","volume":"102","author":"R Choudhary","year":"2009","unstructured":"Choudhary, R., Mahesh, S., Paliwal, J., Jayas, D.: Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosys. Eng. 102(2), 115\u2013127 (2009)","journal-title":"Biosys. Eng."},{"key":"43_CR6","doi-asserted-by":"crossref","unstructured":"Fabiyi, S.D., et al.: Comparative study of PCA and LDA for rice seeds quality inspection. In: 2019 IEEE AFRICON, pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/AFRICON46755.2019.9134059"},{"issue":"1","key":"43_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-019-0476-y","volume":"15","author":"L Feng","year":"2019","unstructured":"Feng, L., Zhu, S., Liu, F., He, Y., Bao, Y., Zhang, C.: Hyperspectral imaging for seed quality and safety inspection: a review. Plant Meth. 15(1), 1\u201325 (2019)","journal-title":"Plant Meth."},{"issue":"12","key":"43_CR8","doi-asserted-by":"publisher","first-page":"443","DOI":"10.3390\/separations9120443","volume":"9","author":"N Hacini","year":"2022","unstructured":"Hacini, N., Djelloul, R., Hadef, A., Samson, M.F., Desclaux, D.: Comparative characterization of grain protein content and composition by chromatography-based separation methods (SE-HPLC and RP-HPLC) of ten wheat varieties grown in different agro-ecological zones of Algeria. Separations 9(12), 443 (2022)","journal-title":"Separations"},{"issue":"2","key":"43_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5(2), 1 (2015)","journal-title":"Int. J. Data Min. Knowl. Manage. Process"},{"issue":"4","key":"43_CR10","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1094\/CCHEM-09-16-0248-R","volume":"94","author":"C Issarny","year":"2017","unstructured":"Issarny, C., Cao, W., Falk, D., Seetharaman, K., Bock, J.E.: Exploring functionality of hard and soft wheat flour blends for improved end-use quality prediction. Cereal Chem. 94(4), 723\u2013732 (2017)","journal-title":"Cereal Chem."},{"key":"43_CR11","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1016\/j.ijbiomac.2018.11.192","volume":"123","author":"M Katyal","year":"2019","unstructured":"Katyal, M., Singh, N., Chopra, N., Kaur, A.: Hard, medium-hard and extraordinarily soft wheat varieties: comparison and relationship between various starch properties. Int. J. Biol. Macromol. 123, 1143\u20131149 (2019)","journal-title":"Int. J. Biol. Macromol."},{"key":"43_CR12","first-page":"1","volume":"2022","author":"A Khatri","year":"2022","unstructured":"Khatri, A., Agrawal, S., Chatterjee, J.M.: Wheat seed classification: utilizing ensemble machine learning approach. Sci. Program. 2022, 1\u20139 (2022)","journal-title":"Sci. Program."},{"issue":"2","key":"43_CR13","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1021\/ac60038a038","volume":"22","author":"PL Kirk","year":"1950","unstructured":"Kirk, P.L.: Kjeldahl method for total nitrogen. Anal. Chem. 22(2), 354\u2013358 (1950)","journal-title":"Anal. Chem."},{"key":"43_CR14","doi-asserted-by":"crossref","unstructured":"Lozano-S\u00e1nchez, J., Borr\u00e1s-Linares, I., Sass-Kiss, A., Segura-Carretero, A.: Chromatographic technique: high-performance liquid chromatography (HPLC). In: Modern Techniques for Food Authentication, pp. 459\u2013526. Elsevier (2018)","DOI":"10.1016\/B978-0-12-814264-6.00013-X"},{"key":"43_CR15","doi-asserted-by":"publisher","first-page":"111318","DOI":"10.1016\/j.postharvbio.2020.111318","volume":"170","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Saeys, W., Kim, M., Peng, Y., Lu, R.: Hyperspectral imaging technology for quality and safety evaluation of horticultural products: a review and celebration of the past 20-year progress. Postharvest Biol. Technol. 170, 111318 (2020)","journal-title":"Postharvest Biol. Technol."},{"issue":"2","key":"43_CR16","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/app8020212","volume":"8","author":"Z Qiu","year":"2018","unstructured":"Qiu, Z., Chen, J., Zhao, Y., Zhu, S., He, Y., Zhang, C.: Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Appl. Sci. 8(2), 212 (2018)","journal-title":"Appl. Sci."},{"issue":"8","key":"43_CR17","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1002\/jsfa.8080","volume":"97","author":"K Sabanci","year":"2017","unstructured":"Sabanci, K., Kayabasi, A., Toktas, A.: Computer vision-based method for classification of wheat grains using artificial neural network. J. Sci. Food Agric. 97(8), 2588\u20132593 (2017)","journal-title":"J. Sci. Food Agric."},{"key":"43_CR18","doi-asserted-by":"crossref","unstructured":"Sharma, A., Singh, T., Garg, N.: Combining near-infrared hyperspectral imaging and ANN for varietal classification of wheat seeds. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), pp. 1103\u20131108. IEEE (2022)","DOI":"10.1109\/ICICICT54557.2022.9917725"},{"issue":"10","key":"43_CR19","doi-asserted-by":"publisher","first-page":"e13821","DOI":"10.1111\/jfpe.13821","volume":"44","author":"T Singh","year":"2021","unstructured":"Singh, T., Garg, N.M., Iyengar, S.R.: Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network. J. Food Process Eng. 44(10), e13821 (2021)","journal-title":"J. Food Process Eng."},{"key":"43_CR20","doi-asserted-by":"publisher","first-page":"110369","DOI":"10.1016\/j.lwt.2020.110369","volume":"136","author":"M Sricharoonratana","year":"2021","unstructured":"Sricharoonratana, M., Thompson, A.K., Teerachaichayut, S.: Use of near infrared hyperspectral imaging as a nondestructive method of determining and classifying shelf life of cakes. LWT 136, 110369 (2021)","journal-title":"LWT"},{"key":"43_CR21","doi-asserted-by":"publisher","unstructured":"Tyagi, N., Raman, B., Garg, N.M.: Varietal classification of wheat seeds using hyperspectral imaging technique and machine learning models. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds.) Computer Vision and Image Processing, CVIP 2022. Communications in Computer and Information Science, vol. 1777, pp. 253\u2013266. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-31417-9_20","DOI":"10.1007\/978-3-031-31417-9_20"},{"issue":"8","key":"43_CR22","doi-asserted-by":"publisher","first-page":"2043","DOI":"10.1007\/s00217-022-04029-4","volume":"248","author":"MF Unlersen","year":"2022","unstructured":"Unlersen, M.F., et al.: CNN-SVM hybrid model for varietal classification of wheat based on bulk samples. Eur. Food Res. Technol. 248(8), 2043\u20132052 (2022)","journal-title":"Eur. Food Res. Technol."},{"issue":"3","key":"43_CR23","doi-asserted-by":"publisher","first-page":"362","DOI":"10.3390\/rs12030362","volume":"12","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Identification of seed maize fields with high spatial resolution and multiple spectral remote sensing using random forest classifier. Remote Sens. 12(3), 362 (2020)","journal-title":"Remote Sens."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:32:24Z","timestamp":1710329544000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_43","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}