{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T04:57:14Z","timestamp":1774241834473,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,10]],"date-time":"2020-05-10T00:00:00Z","timestamp":1589068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1842097"],"award-info":[{"award-number":["1842097"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1830478"],"award-info":[{"award-number":["1830478"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Syngenta Company","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.<\/jats:p>","DOI":"10.3390\/s20092721","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"2721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting"],"prefix":"10.3390","volume":"20","author":[{"given":"Saeed","family":"Khaki","sequence":"first","affiliation":[{"name":"Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA"}]},{"given":"Hieu","family":"Pham","sequence":"additional","affiliation":[{"name":"Syngenta, Slater, IA 50244, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4200-1891","authenticated-orcid":false,"given":"Ye","family":"Han","sequence":"additional","affiliation":[{"name":"Syngenta, Slater, IA 50244, USA"}]},{"given":"Andy","family":"Kuhl","sequence":"additional","affiliation":[{"name":"Syngenta, Slater, IA 50244, USA"}]},{"given":"Wade","family":"Kent","sequence":"additional","affiliation":[{"name":"Syngenta, Slater, IA 50244, USA"}]},{"given":"Lizhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011-3611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,10]]},"reference":[{"key":"ref_1","unstructured":"(2020, May 01). 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