{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:11:30Z","timestamp":1780524690576,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2019-67013-29167"],"award-info":[{"award-number":["2019-67013-29167"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2022-67013-36560"],"award-info":[{"award-number":["2022-67013-36560"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.<\/jats:p>","DOI":"10.3390\/s24072172","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:22:46Z","timestamp":1711628566000},"page":"2172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms"],"prefix":"10.3390","volume":"24","author":[{"given":"Eric","family":"Rodene","sequence":"first","affiliation":[{"name":"Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"},{"name":"Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9010-0210","authenticated-orcid":false,"given":"Gayara Demini","family":"Fernando","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-9279","authenticated-orcid":false,"given":"Ved","family":"Piyush","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Ge","sequence":"additional","affiliation":[{"name":"Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"},{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James C.","family":"Schnable","sequence":"additional","affiliation":[{"name":"Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"},{"name":"Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Souparno","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinliang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"},{"name":"Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics\u2014Technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"538244","DOI":"10.3389\/fpls.2020.00681","article-title":"Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops","volume":"11","author":"Moreira","year":"2020","journal-title":"Front. 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