{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T04:05:05Z","timestamp":1773201905260,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy","award":["DE-AR0000593"],"award-info":[{"award-number":["DE-AR0000593"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.<\/jats:p>","DOI":"10.3390\/rs13152881","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T04:29:14Z","timestamp":1627014554000},"page":"2881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Advancing Tassel Detection and Counting: Annotation and Algorithms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8763-0405","authenticated-orcid":false,"given":"Azam","family":"Karami","sequence":"first","affiliation":[{"name":"Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Karoll","family":"Quijano","sequence":"additional","affiliation":[{"name":"Department of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Melba","family":"Crawford","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA"},{"name":"School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","first-page":"87","article-title":"In-field automatic detection of maize tassels using computer vision","volume":"8","author":"Ji","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0396-x","article-title":"Evaluating maize phenotype dynamics under drought stress using terrestrial LiDAR","volume":"15","author":"Su","year":"2019","journal-title":"Plant Methods"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13007-015-0047-9","article-title":"Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images","volume":"11","author":"Guo","year":"2015","journal-title":"Plant Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4238701","DOI":"10.34133\/2021\/4238701","article-title":"Detection of the progression of anthesis in field-grown maize tassels: A case study","volume":"2021","author":"Mirnezami","year":"2021","journal-title":"Plant Phenomics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5872","DOI":"10.1109\/JSTARS.2020.3025790","article-title":"Automatic plant counting and location based on a few-shot learning technique","volume":"13","author":"Karami","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-017-0172-8","article-title":"TIPS: A system for automated image-based phenotyping of maize tassels","volume":"13","author":"Gage","year":"2017","journal-title":"Plant Methods"},{"key":"ref_8","first-page":"89210Z","article-title":"An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency","volume":"Volume 8921","author":"Ye","year":"2013","journal-title":"Remote Sensing Image Processing, Geographic Information Systems, International Society for Optics and Photonics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7390","DOI":"10.1016\/j.eswa.2014.06.013","article-title":"Detecting corn tassels using computer vision and support vector machines","volume":"41","author":"Kavdir","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Junior, J.M., Ramos, A.P.M., Jorge, L.A.C., Fatholahi, S.N., Silva, J.A., Matsubara, E.T., Gon\u00e7alves, W.N., Pistori, H., and Li, J. (2021). A review on deep learning in UAV remote sensing. arXiv.","DOI":"10.1016\/j.jag.2021.102456"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karami, A., Crawford, M., and Delp, E.J. (October, January 26). A weakly supervised deep learning approach for plant center detection and counting. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324354"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s11119-020-09725-3","article-title":"Automated crop plant counting from very high-resolution aerial imagery","volume":"21","author":"Valente","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-018-0366-8","article-title":"Detection and analysis of wheat spikes using convolutional neural networks","volume":"14","author":"Hasan","year":"2018","journal-title":"Plant Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1525874","DOI":"10.34133\/2019\/1525874","article-title":"A weakly supervised deep learning framework for sorghum head detection and counting","volume":"2019","author":"Ghosal","year":"2019","journal-title":"Plant Phenomics"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s13007-020-00651-z","article-title":"Maize tassels detection: A benchmark of the state of the art","volume":"16","author":"Zou","year":"2020","journal-title":"Plant Methods"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s13007-017-0224-0","article-title":"TasselNet: Counting maize tassels in the wild via local counts regression network","volume":"13","author":"Lu","year":"2017","journal-title":"Plant Methods"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/s13007-019-0537-2","article-title":"TasselNetv2: In-field counting of wheat spikes with context-augmented local regression networks","volume":"15","author":"Xiong","year":"2019","journal-title":"Plant Methods"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.3389\/fpls.2020.541960","article-title":"TasselNetV2+: A fast implementation for high-throughput plant counting from high-resolution RGB imagery","volume":"11","author":"Lu","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 21\u201326). Focal loss for dense object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_21","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020, January 21\u201322). Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Proceedings of the 2020 Conference on Neural Information Processing Systems, NeurIPS, Vancouver, BC, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized focal loss V2: Learning reliable localization quality estimation for dense object detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"ref_23","unstructured":"Shinya, Y. (2021). USB: Universal-scale object detection benchmark. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Song, G., Liu, Y., and Wang, X. (2020, January 14\u201319). Revisiting the sibling had in object detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Z., Cao, S., Yang, Y., and Kitani, K. (2020). Rethinking transformer-based set prediction for object detection. arXiv.","DOI":"10.1109\/ICCV48922.2021.00359"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2020). DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution. arXiv.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cen, C., Che, Y., Ke, R., Ma, Y., and Ma, Y. (2020). Detection of maize tassels from UAV RGB imagery with faster R-CNN. Remote Sens., 12.","DOI":"10.3390\/rs12020338"},{"key":"ref_29","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., and Li, S.Z. (2017, January 1\u20134). Faceboxes: A CPU real-time face detector with high accuracy. Proceedings of the International Joint Conference on Biometrics, Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272675"},{"key":"ref_31","first-page":"4321","article-title":"UAV based remote sensing for tassel detection and growth stage estimation of maize crop using F-RCNN","volume":"3","author":"Kumar","year":"2019","journal-title":"Comput. Vis. Probl. Plant Phenotyping"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q., and Yan, J. (2019, January 16\u201320). Grid R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00754"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","article-title":"Foveabox: Beyound anchor-based object detection","volume":"29","author":"Kong","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Krahenbuhl, P. (2019, January 16\u201320). Bottom-up object detection by grouping extreme and center points. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref_36","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, Y.C., Zhou, T., Wang, T., Crawford, M., and Habib, A. (2021). New orthophoto generation strategies from UAV and ground remote sensing platforms for high-throughput phenotyping. Remote Sens., 13.","DOI":"10.3390\/rs13050860"},{"key":"ref_38","unstructured":"(2021, April 22). The Genomes to Fields Initiative (G2F). Available online: https:\/\/www.genomes2fields.org\/resources\/."},{"key":"ref_39","unstructured":"Wada, K. (2016, May 09). Labelme: Image Polygonal Annotation with Python. Available online: https:\/\/github.com\/wkentaro\/labelme."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2019). Cascade R-CNN: High quality object detection and instance segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 21\u201326). Deformable convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_42","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFS. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Kokkinos, I., and Savalle, P.A. (2015, January 7\u201312). Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298636"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2881\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:33:42Z","timestamp":1760164422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":44,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152881"],"URL":"https:\/\/doi.org\/10.3390\/rs13152881","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}