{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:41:07Z","timestamp":1768693267724,"version":"3.49.0"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014814","name":"Ministry of Agriculture, Republic of Indonesia","doi-asserted-by":"crossref","award":["052-524111-2020"],"award-info":[{"award-number":["052-524111-2020"]}],"id":[{"id":"10.13039\/100014814","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,4,26]]},"abstract":"<jats:p>Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant\/off-type plants. Female and contaminant\/off-type plants\u2019 tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research\u2019s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models\u2019 performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type\/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders\/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.<\/jats:p>","DOI":"10.1155\/2022\/6588949","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T23:20:05Z","timestamp":1651015205000},"page":"1-15","source":"Crossref","is-referenced-by-count":3,"title":["Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1842-9230","authenticated-orcid":true,"given":"M.","family":"Aqil","sequence":"first","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5546-9485","authenticated-orcid":true,"given":"M.","family":"Azrai","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2361-4657","authenticated-orcid":true,"given":"M. J.","family":"Mejaya","sequence":"additional","affiliation":[{"name":"Indonesian Legumes and Tuber Crops Research Institute, Malang, Indonesia"}]},{"given":"N. A.","family":"Subekti","sequence":"additional","affiliation":[{"name":"Indonesian Center for Food Crops Research and Development, Bogor, Indonesia"}]},{"given":"F.","family":"Tabri","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"N. N.","family":"Andayani","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"Rahma","family":"Wati","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"S.","family":"Panikkai","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1921-8785","authenticated-orcid":true,"given":"S.","family":"Suwardi","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"Z.","family":"Bunyamin","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"E.","family":"Roy","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"M.","family":"Muslimin","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"given":"M.","family":"Yasin","sequence":"additional","affiliation":[{"name":"Indonesian Cereals Research Institute, Maros, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4685-6309","authenticated-orcid":true,"given":"E.","family":"Prakasa","sequence":"additional","affiliation":[{"name":"National Research and Innovation Agency, Bandung, Indonesia"}]}],"member":"311","reference":[{"key":"1","volume-title":"Cara Cepat Swasembada Jagung","author":"K. A. Sulaiman"},{"key":"2","volume-title":"Strategies for Strengthening and Scaling Up Community-Based Seed Production","author":"P. S. Setimela","year":"2006"},{"key":"3","first-page":"1237","article-title":"IJCAI11-210.pdf","author":"D. C. Cires"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-017-0224-0"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1094\/phyto-11-16-0417-r"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-4666-9435-4.ch014"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-71898-8"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.9734\/jeai\/2018\/43464"},{"key":"9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s20092721","article-title":"Convolutional neural networks for image-based corn kernel detection and counting","volume":"20","author":"S. Khaki","year":"2020","journal-title":"Sensors (Switzerland)"},{"key":"10","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-9326-7","volume-title":"Ensemble Machine Learning: Methods and Applications","author":"C. Zhang","year":"2012"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2021.0120516"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.118271"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-12397-x"},{"key":"14","doi-asserted-by":"crossref","article-title":"Rethinking the inception architecture for computer vision","author":"C. Szegedy","DOI":"10.1109\/CVPR.2016.308"},{"key":"15","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","author":"F. N. Iandola","year":"2016"},{"key":"16","article-title":"Stacked generalization neural networks 5 241\u201359","author":"D. H. Wolpert","year":"1992"},{"key":"17","article-title":"Combining information extraction systems using voting and stacked generalization","volume":"6","author":"G. Sigletos","year":"2005","journal-title":"Journal of Machine Learning Research"},{"key":"18","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","author":"M. Tan"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1007\/s00122-005-0133-x"},{"key":"20","doi-asserted-by":"crossref","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"D. W. Hosmer","year":"2013"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/515918"},{"key":"22","doi-asserted-by":"crossref","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Z. Zhou","year":"2012"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1023\/b:mach.0000015881.36452.6e"},{"key":"24","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"M. Abadi","year":"2016"},{"key":"25","article-title":"Self-training with noisy student improves imagenet classification","author":"Q. Xie"},{"key":"26","article-title":"Anon ensemble methods: bagging, boosting and stacking | by joseph rocca | towards data science"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.01120"},{"key":"28","first-page":"1","article-title":"A new approach of ensemble learning technique to resolve the uncertainties of paddy area through image classification","volume":"12","author":"T. C. Lei","year":"2020","journal-title":"Remote Sensor"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1556\/crc.34.2006.1.158"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2015.08.027"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2009.167"},{"key":"32","article-title":"Privacy preserving crowd monitoring: counting people without people models or tracking","author":"A. B. Chan"},{"key":"33","doi-asserted-by":"crossref","article-title":"Learning to count leaves in rosette plants 1","author":"M. V. Giuffrida","DOI":"10.5244\/C.29.CVPPP.1"},{"key":"34","article-title":"Learning to count with regression forest and structured labels","author":"L. Fiaschi"},{"key":"35","doi-asserted-by":"crossref","article-title":"Cross-scene crowd counting via deep convolutional neural networks","author":"C. Zhang","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"36","doi-asserted-by":"crossref","article-title":"Counting in the wild","author":"C. Arteta","DOI":"10.1007\/978-3-319-46478-7_30"},{"key":"37","volume-title":"Interactive Object Counting BT-Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling","author":"C. Arteta","year":"2014"},{"key":"38","article-title":"Count-ception: counting by fully convolutional redundant counting","author":"J. P. Cohen"},{"key":"39","article-title":"Visualizing and understanding convolutional networks","author":"M. D. Zeiler"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2010.2042645"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/6588949.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/6588949.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/6588949.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T23:20:20Z","timestamp":1651015220000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2022\/6588949\/"}},"subtitle":[],"editor":[{"given":"Soumya","family":"Ranjan Nayak","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,26]]},"references-count":40,"alternative-id":["6588949","6588949"],"URL":"https:\/\/doi.org\/10.1155\/2022\/6588949","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2022,4,26]]}}}