{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:16:30Z","timestamp":1783530990531,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T00:00:00Z","timestamp":1613952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves\u2019 images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.<\/jats:p>","DOI":"10.3390\/sym13020356","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T20:42:51Z","timestamp":1614026571000},"page":"356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0385-3933","authenticated-orcid":false,"given":"Shubham","family":"Mahajan","sequence":"first","affiliation":[{"name":"School of Electronics &amp; Communication, Shri Mata Vaishno Devi University, Katra 182320, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8623-5470","authenticated-orcid":false,"given":"Akshay","family":"Raina","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shri Mata Vaishno Devi University, Katra 182320, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Zhi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computing, University of Eastern Finland, 70210 Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Kant Pandit","sequence":"additional","affiliation":[{"name":"School of Electronics &amp; Communication, Shri Mata Vaishno Devi University, Katra 182320, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"466","DOI":"10.3390\/app10020466","article-title":"Data enhancement for plant disease classification using generated lesions","volume":"10","author":"Sun","year":"2020","journal-title":"Appl. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhong, W., and Li, F. (2020). Leaf segmentation and classification with a complicated background using deep learning. Agronomy, 10.","DOI":"10.3390\/agronomy10111721"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cui, J., Wang, Z., Kang, J., and Min, Y. (2020). Leaf Image Recognition Based on Bag of Features. Appl. Sci., 10.","DOI":"10.3390\/app10155177"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Azlah, M.A., Chua, L.S., Rahmad, F.R., Abdullah, F.I., and Wan Alwi, S.R. (2019). Review on techniques for plant leaf classification and recognition. Computers, 8.","DOI":"10.3390\/computers8040077"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163912","DOI":"10.1109\/ACCESS.2019.2952176","article-title":"Plant Species Recognition Using Morphological Features and Adaptive Boosting Methodology","volume":"7","author":"Kumar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.5391\/IJFIS.2017.17.1.26","article-title":"Plant leaf recognition using a convolution neural network","volume":"17","author":"Jeon","year":"2017","journal-title":"Int. J. Fuzzy Log. Intell. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pankaja, K., and Suma, V. (2018, January 11\u201312). Leaf Recognition and Classification Using GLCM and Hierarchical Centroid Based Technique. Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.","DOI":"10.1109\/ICIRCA.2018.8597184"},{"key":"ref_8","first-page":"436","article-title":"Leaf Color, Area and Edge features-based approach for Identification of Indian Medicinal Plants","volume":"3","author":"Kumar","year":"2012","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_9","first-page":"113","article-title":"Performance improvement of leaf identification system using principal component analysis","volume":"44","author":"Kadir","year":"2012","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Priya, C.A., Balasaravanan, T., and Thanamani, A.S. (2012, January 21\u201323). An efficient leaf recognition algorithm for plant classification using support vector machine. Proceedings of the InInternational conference on pattern recognition, informatics and medical engineering (PRIME-2012), Salem, India.","DOI":"10.1109\/ICPRIME.2012.6208384"},{"key":"ref_11","first-page":"45","article-title":"A combined color, texture and edge features-based approach for identification and classification of indian medicinal plants","volume":"6","author":"Anami","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_12","first-page":"10","article-title":"Leaf recognition algorithm using neural network-based image processing","volume":"2","author":"Sambhaji","year":"2014","journal-title":"Asian J. Eng. Technol. Innov."},{"key":"ref_13","first-page":"2017","article-title":"Deep learning for plant identification in natural environment","volume":"22","author":"Sun","year":"2017","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Duan, K.B., and Keerthi, S.S. (2005). Which is the best multiclass SVM method? An empirical study. International Workshop on Multiple Classifier Systems, Proceedings of the Multiple Classifier Systems, Springer.","DOI":"10.1007\/11494683_28"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hastie, T., and Tibshirani, R. (1998). Classification by pairwise coupling. Advances in Neural Information Processing Systems, MIT Press.","DOI":"10.1214\/aos\/1028144844"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_18","unstructured":"(2020, January 04). Boosting Algorithms: AdaBoost, Gradient Boosting and XGBoost. Available online: hackernoon.com."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class adaboost","volume":"2","author":"Hastie","year":"2009","journal-title":"Stat. Its Interface"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, T.H., Park, D.C., Woo, D.M., Jeong, T., and Min, S.Y. (2011). Multi-class classifier-based adaboost algorithm. International Conference on Intelligent Science and Intelligent Data Engineering, Proceedings of the Intelligent Science and Intelligent Data Engineering, Springer.","DOI":"10.1007\/978-3-642-31919-8_16"},{"key":"ref_21","unstructured":"(2021, January 10). CS231n: Convolutional Neural Networks for Visual Recognition. Available online: http:\/\/cs231n.github.io\/transfer-learning\/."},{"key":"ref_22","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. arXiv."},{"key":"ref_23","unstructured":"(2021, January 05). Plant Leaf Recognition. Available online: http:\/\/cs229.stanford.edu\/proj2016\/report\/LiuHuang-PlantLeafRecognition-report.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.engappai.2007.07.001","article-title":"AdaBoost with SVM-based component classifiers","volume":"21","author":"Li","year":"2008","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","unstructured":"Govindaraj, D. (2016). Can Boosting with SVM as Week Learners Help?. arXiv, preprint."},{"key":"ref_27","unstructured":"Garc\u00eda, E., and Lozano, F. (2020, January 10). Boosting Support Vector Machines. MLDM Posters, Available online: https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0957417420301457."},{"key":"ref_28","unstructured":"(2021, January 02). Flavia (at a Glance). Available online: http:\/\/flavia.sourceforge.net\/."},{"key":"ref_29","first-page":"18","article-title":"Identifying efficient kernel function in multiclass support vector machines","volume":"28","author":"Sangeetha","year":"2011","journal-title":"Int. J. Comput. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/356\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:27:56Z","timestamp":1760160476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,22]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["sym13020356"],"URL":"https:\/\/doi.org\/10.3390\/sym13020356","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,22]]}}}