{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T21:52:10Z","timestamp":1776808330122,"version":"3.51.2"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51875217"],"award-info":[{"award-number":["No. 51875217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Science Foundation for Young Scientists of China","award":["No. 31801258"],"award-info":[{"award-number":["No. 31801258"]}]},{"name":"the National Key R&amp;D Program of China","award":["No. 2018 YFD0200303"],"award-info":[{"award-number":["No. 2018 YFD0200303"]}]},{"name":"the Earmarked Fund for Modern Agro-industry Technology Research System","award":["No. CARS-01-43"],"award-info":[{"award-number":["No. CARS-01-43"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.<\/jats:p>","DOI":"10.3390\/s21010281","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T08:35:19Z","timestamp":1609749319000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Automated Counting Grains on the Rice Panicle Based on Deep Learning Method"],"prefix":"10.3390","volume":"21","author":[{"given":"Ruoling","family":"Deng","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Ming","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Xunan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Kemoh","family":"Bangura","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Qian","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Modern Educational Technology Center, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Long","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.3389\/fpls.2019.00543","article-title":"Exploring the Relationships between Yield and Yield-Related Traits for Rice Varieties Released in China from 1978 to 2017","volume":"10","author":"Li","year":"2019","journal-title":"Front. 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