{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T21:45:33Z","timestamp":1785361533499,"version":"3.55.0"},"reference-count":23,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,13]],"date-time":"2019-07-13T00:00:00Z","timestamp":1562976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFD0700303"],"award-info":[{"award-number":["2016YFD0700303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31401287"],"award-info":[{"award-number":["31401287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Public Projects of Zhejiang Province","award":["2017C32024"],"award-info":[{"award-number":["2017C32024"]}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["6182011"],"award-info":[{"award-number":["6182011"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61661136003"],"award-info":[{"award-number":["61661136003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471351"],"award-info":[{"award-number":["41471351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.<\/jats:p>","DOI":"10.3390\/s19143106","type":"journal-article","created":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T04:55:27Z","timestamp":1563166527000},"page":"3106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform"],"prefix":"10.3390","volume":"19","author":[{"given":"Chengquan","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbao","family":"Ye","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Hua","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. 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China, Beijing Research Center for Information Technology in Agriculture, Beijing 100089, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100089, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s00484-018-1583-6","article-title":"Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of india","volume":"62","author":"Bappa","year":"2018","journal-title":"Int. J. Biometeorol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jang, S., Lee, Y., Lee, G., Seo, J., Lee, D., and Yu, Y. (2018). Association between sequence variants in panicle development genes and the number of spikelets per panicle in rice. BMC Genet., 19.","DOI":"10.1186\/s12863-017-0591-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1016\/S2095-3119(17)61667-8","article-title":"Erect panicle super rice varieties enhance yield by harvest index advantages in high nitrogen and density conditions","volume":"16","author":"Tang","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Phadikar, S., and Sil, J. (2008, January 24\u201327). Rice disease identification using pattern recognition techniques. Proceedings of the 2008 11th International Conference on Computer and Information Technology, Khulna, Bangladesh.","DOI":"10.1109\/ICCITECHN.2008.4803079"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.compag.2013.08.006","article-title":"Rice panicle length measuring system based on dual-camera imaging","volume":"98","author":"Huang","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","first-page":"22","article-title":"Wheat ear counting in-field conditions: High throughput and low-cost approach using RGB images","volume":"2018","author":"Kefauver","year":"2018","journal-title":"Plant Methods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"023101","DOI":"10.1117\/1.OE.56.2.023101","article-title":"Underwater color image segmentation method via RGB channel fusion","volume":"56","author":"Li","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A survey on deep learning techniques for image and video semantic segmentation","volume":"70","author":"Oprea","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"866","DOI":"10.3389\/fpls.2018.00866","article-title":"Deep learning: Individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms","volume":"9","author":"Jin","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s13007-017-0254-7","article-title":"Panicle-seg: A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization","volume":"13","author":"Xiong","year":"2017","journal-title":"Plant Methods"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., and Sheppard, C. (2017). Deep count: Fruit counting based on deep simulated learning. Sensors, 17.","DOI":"10.3390\/s17040905"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.neucom.2017.01.018","article-title":"Plant identification using deep neural networks via optimization of transfer learning parameters","volume":"235","author":"Ghazi","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2016.12.013","article-title":"Data augmentation for unbalanced face recognition training sets","volume":"235","author":"Leng","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","article-title":"Anatomically constrained neural networks (ACNNs): Application to cardiac image enhancement and segmentation","volume":"37","author":"Oktay","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/nrgastro.2017.18","article-title":"Advances in image enhancement in colonoscopy for detection of adenomas","volume":"14","author":"Matsuda","year":"2017","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"ref_16","first-page":"1052","article-title":"Image super-resolution reconstruction using the high-order derivative interpolation associated with fractional filter functions","volume":"10","author":"Deyun","year":"2017","journal-title":"IET Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_18","first-page":"1","article-title":"Pedestrian movement direction recognition using convolutional neural networks","volume":"99","author":"Cazorla","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/TIP.2017.2656474","article-title":"Scene text detection and segmentation based on cascaded convolution neural networks","volume":"26","author":"Tang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"682","DOI":"10.3389\/fnins.2017.00682","article-title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification","volume":"11","author":"Rueckauer","year":"2017","journal-title":"Front. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.3390\/s17092022","article-title":"A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition","volume":"17","author":"Alvaro","year":"2017","journal-title":"Sensors"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.cviu.2007.08.003","article-title":"Image segmentation evaluation: A survey of unsupervised methods","volume":"110","author":"Zhang","year":"2008","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:05:27Z","timestamp":1760187927000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,13]]},"references-count":23,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143106"],"URL":"https:\/\/doi.org\/10.3390\/s19143106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,13]]}}}