{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:14:44Z","timestamp":1768464884077,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870347"],"award-info":[{"award-number":["31870347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002860","name":"China Sponsorship Council","doi-asserted-by":"publisher","award":["201806320107"],"award-info":[{"award-number":["201806320107"]}],"id":[{"id":"10.13039\/501100002860","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.<\/jats:p>","DOI":"10.3390\/s21020507","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T21:50:54Z","timestamp":1610574654000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4594-4158","authenticated-orcid":false,"given":"Le","family":"Wang","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lie","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"567","DOI":"10.2298\/BAH1504567M","article-title":"The effect of crop density on yield of forage maize","volume":"31","author":"Mandic","year":"2015","journal-title":"Biotechnol. 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