{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:38:19Z","timestamp":1768282699031,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework.<\/jats:p>","DOI":"10.3390\/jimaging7090171","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T04:07:28Z","timestamp":1630382848000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["On the Efficacy of Handcrafted and Deep Features for Seed Image Classification"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6571-3816","authenticated-orcid":false,"given":"Andrea","family":"Loddo","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4641-0307","authenticated-orcid":false,"given":"Cecilia","family":"Di Ruberto","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105661","DOI":"10.1016\/j.compag.2020.105661","article-title":"Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease","volume":"177","author":"Barman","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103530","DOI":"10.1016\/j.compbiomed.2019.103530","article-title":"Detection of red and white blood cells from microscopic blood images using a region proposal approach","volume":"116","author":"Loddo","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Campanile, G., Di Ruberto, C., and Loddo, A. (2019, January 12\u201314). An Open Source Plugin for Image Analysis in Biology. Proceedings of the 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Napoli, Italy.","DOI":"10.1109\/WETICE.2019.00042"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahmad, N., Asghar, S., and Gillani, S.A. (2021). Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis. Comput., 1\u201320.","DOI":"10.1007\/s00371-021-02153-y"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compag.2017.02.009","article-title":"Phenotypic identification of plum varieties (Prunus domestica L.) by endocarps morpho-colorimetric and textural descriptors","volume":"136","author":"Sarigu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106269","DOI":"10.1016\/j.compag.2021.106269","article-title":"A novel deep learning based approach for seed image classification and retrieval","volume":"187","author":"Loddo","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s00334-014-0512-9","article-title":"Earliest evidence of a primitive cultivar of Vitis vinifera L. during the Bronze Age in Sardinia (Italy)","volume":"24","author":"Ucchesu","year":"2015","journal-title":"Veg. Hist. Archaeobot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ucchesu, M., Orr\u00f9, M., Grillo, O., Venora, G., Paglietti, G., Ardu, A., and Bacchetta, G. (2016). Predictive method for correct identification of archaeological charred grape seeds: Support for advances in knowledge of grape domestication process. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0149814"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s00334-017-0622-2","article-title":"First finds of Prunus domestica L. in Italy from the Phoenician and Punic periods (6th\u20132nd centuries bc)","volume":"26","author":"Ucchesu","year":"2017","journal-title":"Veg. Hist. Archaeobot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/plb.12529","article-title":"Inter- and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques","volume":"19","author":"Grillo","year":"2017","journal-title":"Plant Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1111\/plb.12481","article-title":"Morpho-colorimetric characterisation of Malva alliance taxa by seed image analysis","volume":"19","author":"Grillo","year":"2017","journal-title":"Plant Biol."},{"key":"ref_13","unstructured":"(2021, July 07). ImageJ. Available online: https:\/\/imagej.net\/ImageJ."},{"key":"ref_14","unstructured":"Landini, G. (2008, January 7\u20138). Advanced shape analysis with ImageJ. Proceedings of the 2th ImageJ User and Developer Conference, Luxembourg."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Harland, L., and Forster, M. (2012). Open source software for image processing and analysis: Picture this with ImageJ. Open Source Software in Life Science Research, Woodhead Publishing.","DOI":"10.1533\/9781908818249"},{"key":"ref_16","unstructured":"Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA, 3\u20136 December 2012, Neural Information Processing Systems Foundation, Inc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/j.imavis.2008.10.009","article-title":"Decomposition of Two-Dimensional Shapes for Efficient Retrieval","volume":"27","author":"Cinque","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nalpantidis, L., Kr\u00fcger, V., Eklundh, J.O., and Gasteratos, A. (2015). Comparison of Statistical Features for Medical Colour Image Classification. Computer Vision Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-20904-3"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Loddo, A., Di Ruberto, C., Vale, A., Ucchesu, M., Soares, J., and Bacchetta, G. (2021). An effective and friendly tool for seed image analysis. arXiv.","DOI":"10.1007\/s00371-021-02333-w"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gulzar, Y., Hamid, Y., Soomro, A.B., Alwan, A.A., and Journaux, L. (2020). A convolution neural network-based seed classification system. Symmetry, 12.","DOI":"10.3390\/sym12122018"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.compag.2018.12.001","article-title":"Using Deep Convolutional Neural Network for oak acorn viability recognition based on color images of their sections","volume":"156","author":"Przybylo","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","unstructured":"Di Ruberto, C., and Putzu, L. (2014, January 5\u20138). A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hall, D., McCool, C., Dayoub, F., Sunderhauf, N., and Upcroft, B. (2015, January 5\u20139). Evaluation of Features for Leaf Classification in Challenging Conditions. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.111"},{"key":"ref_24","first-page":"570","article-title":"A Mobile Application for Leaf Detection in Complex Background Using Saliency Maps. Advanced Concepts for Intelligent Vision Systems","volume":"Volume 10016","author":"Distante","year":"2016","journal-title":"Lecture Notes in Computer Science, Proceedings of the 17th International Conference, ACIVS 2016, Lecce, Italy, 24\u201327 October 2016"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106185","DOI":"10.1016\/j.compag.2021.106185","article-title":"Recognition of carrot appearance quality based on deep feature and support vector machine","volume":"186","author":"Zhu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Comput. Intell. Neurosci., 2016.","DOI":"10.1155\/2016\/3289801"},{"key":"ref_28","unstructured":"Amara, J., Bouaziz, B., and Algergawy, A. (2017). A deep learning-based approach for banana leaf diseases classification. Lecture Notes in Informatics (LNI), Gesellschaft fur Informatik (GI)."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gajjar, R., Gajjar, N., Thakor, V.J., Patel, N.P., and Ruparelia, S. (2021). Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis. Comput., 1\u201316.","DOI":"10.1007\/s00371-021-02164-9"},{"key":"ref_30","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Junos, M.H., Khairuddin, A.S.M., Thannirmalai, S., and Dahari, M. (2021). Automatic detection of oil palm fruits from UAV images using an improved YOLO model. Vis. Comput., 1\u201315.","DOI":"10.1049\/ipr2.12181"},{"key":"ref_32","unstructured":"Valstar, M.F., French, A.P., and Pridmore, T.P. (2014, January 1\u20135). Return of the Devil in the Details: Delving Deep into Convolutional Nets. Proceedings of the British Machine Vision Conference, BMVC 2014, Nottingham, UK."},{"key":"ref_33","unstructured":"(2021, August 13). Canada Dataset. Available online: https:\/\/inspection.canada.ca\/active\/netapp\/idseed\/idseed_gallerye.aspx?itemsNum=-1&famkey=&family=&keyword=&letter=A."},{"key":"ref_34","unstructured":"Vale, A.M.P.G., Ucchesu, M., Ruberto, C.D., Loddo, A., Soares, J.M., and Bacchetta, G. (2020). A new automatic approach to seed image analysis: From acquisition to segmentation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carrasco-Ochoa, J.A., Mart\u00ednez-Trinidad, J.F., Rodr\u00edguez, J.S., and di Baja, G.S. (2013). Assessments Metrics for Multi-class Imbalance Learning: A Preliminary Study. Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-38989-4"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_40","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26995","DOI":"10.1007\/s11042-020-09292-9","article-title":"Convolutional neural networks for relevance feedback in content based image retrieval","volume":"79","author":"Putzu","year":"2020","journal-title":"Mult. Tools Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"012177","DOI":"10.1088\/1742-6596\/803\/1\/012177","article-title":"A machine learning approach for grain crop\u2019s seed classification in purifying separation","volume":"803","author":"Vlasov","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_44","first-page":"2760","article-title":"Comparisons of classification algorithms on seeds dataset using machine learning algorithm","volume":"7","author":"Agrawal","year":"2018","journal-title":"Compusoft"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/9\/171\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:52:58Z","timestamp":1760165578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/9\/171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,31]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["jimaging7090171"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7090171","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,31]]}}}