{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:55:44Z","timestamp":1783490144932,"version":"3.55.0"},"reference-count":104,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T00:00:00Z","timestamp":1623456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.<\/jats:p>","DOI":"10.3390\/rs13122308","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:25:46Z","timestamp":1623709546000},"page":"2308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping"],"prefix":"10.3390","volume":"13","author":[{"given":"Masoomeh","family":"Aslahishahri","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin G.","family":"Stanley","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hema","family":"Duddu","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Shirtliffe","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally","family":"Vail","sequence":"additional","affiliation":[{"name":"Agriculture and Agri-Food Canada, Saskatoon, SK S7N 0X2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian","family":"Stavness","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fr\u00f3na, D., Szender\u00e1k, J., and Harangi-R\u00e1kos, M. 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