{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:11:48Z","timestamp":1760346708838,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,13]],"date-time":"2018-07-13T00:00:00Z","timestamp":1531440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method.<\/jats:p>","DOI":"10.3390\/s18072270","type":"journal-article","created":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T04:05:33Z","timestamp":1531713933000},"page":"2270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse"],"prefix":"10.3390","volume":"18","author":[{"given":"Jing","family":"Zhou","sequence":"first","affiliation":[{"name":"Division of Food Systems and Bioengineering, University of Missouri, Columbia, MO 65211, USA"}]},{"given":"Xiuqing","family":"Fu","sequence":"additional","affiliation":[{"name":"Division of Food Systems and Bioengineering, University of Missouri, Columbia, MO 65211, USA"},{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Leon","family":"Schumacher","sequence":"additional","affiliation":[{"name":"Division of Food Systems and Bioengineering, University of Missouri, Columbia, MO 65211, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7127-1428","authenticated-orcid":false,"given":"Jianfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Division of Food Systems and Bioengineering, University of Missouri, Columbia, MO 65211, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2014.01.010","article-title":"High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants","volume":"121","author":"Paulus","year":"2014","journal-title":"Biosyst. 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