{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:49Z","timestamp":1760242009528,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T00:00:00Z","timestamp":1542758400000},"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>Canopy edge profile detection is a critical component of plant recognition in variable-rate spray control systems. The accuracy of a high-speed 270\u00b0 radial laser sensor was evaluated in detecting the surface edge profiles of six complex-shaped objects. These objects were toy balls with a pink smooth surface, light brown rectangular cardboard boxes, black and red texture surfaced basketballs, white smooth cylinders, and two different sized artificial plants. Evaluations included reconstructed three-dimensional (3-D) images for the object surfaces with the data acquired from the laser sensor at four different detection heights (0.25, 0.50, 0.75, and 1.00 m) above each object, five sensor travel speeds (1.6, 2.4, 3.2, 4.0, and 4.8 km h\u22121), and 8 to 15 horizontal distances to the sensor ranging from 0 to 3.5 m. Edge profiles of the six objects detected with the laser sensor were compared with images taken with a digital camera. The edge similarity score (ESS) was significantly affected by the horizontal distances of the objects, and the influence became weaker when the objects were placed closer to each other. The detection heights and travel speeds also influenced the ESS slightly. The overall average ESS ranged from 0.38 to 0.95 for all the objects under all the test conditions, thereby providing baseline information for the integration of the laser sensor into future development of greenhouse variable-rate spray systems to improve pesticide, irrigation, and nutrition application efficiencies through watering booms.<\/jats:p>","DOI":"10.3390\/s18114060","type":"journal-article","created":{"date-parts":[[2018,11,22]],"date-time":"2018-11-22T09:18:25Z","timestamp":1542878305000},"page":"4060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor"],"prefix":"10.3390","volume":"18","author":[{"given":"Tingting","family":"Yan","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"},{"name":"USDA-ARS Application Technology Research Unit, Wooster, OH 44691, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heping","family":"Zhu","sequence":"additional","affiliation":[{"name":"USDA-ARS Application Technology Research Unit, Wooster, OH 44691, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Ling","sequence":"additional","affiliation":[{"name":"Department of Food, Agricultural and Biological Engineering, The Ohio State University, Wooster, OH 43210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.13031\/2013.20130","article-title":"Autonomous speed sprayer guidance using machine vision and fuzzy logic","volume":"42","author":"Cho","year":"1999","journal-title":"Trans. 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