{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:59:15Z","timestamp":1781269155459,"version":"3.54.1"},"reference-count":91,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture, Agricultural Research Service","doi-asserted-by":"publisher","award":["58-6064-8-023"],"award-info":[{"award-number":["58-6064-8-023"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model\u2019s performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7\u2019s pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model\u2019s performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.<\/jats:p>","DOI":"10.3390\/s23146506","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T01:02:23Z","timestamp":1689728543000},"page":"6506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7577-0232","authenticated-orcid":false,"given":"Jithin","family":"Mathew","sequence":"first","affiliation":[{"name":"Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7141-7415","authenticated-orcid":false,"given":"Nadia","family":"Delavarpour","sequence":"additional","affiliation":[{"name":"Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7535-3404","authenticated-orcid":false,"given":"Carrie","family":"Miranda","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Stenger","sequence":"additional","affiliation":[{"name":"North Dakota Agricultural Weather Network, School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justice","family":"Aduteye","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Earth University, San Jose 4442-1000, Costa Rica"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3964-6904","authenticated-orcid":false,"given":"Paulo","family":"Flores","sequence":"additional","affiliation":[{"name":"Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s12571-010-0108-x","article-title":"Crops that feed the World 2. 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