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The morphological features (length, width, and perimeter for the wheat stem and ear) were extracted by a computer vision system whose performance was evaluated by correlating the measured and estimated perimeter and length of the wheat stem at an <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:msup><mml:mrow><mml:mi>R<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:msup><\/mml:math> of 0.9609 and 0.9779, respectively. Six regression models were developed based on the extracted features. The linear regression based on the wet weight of the stem, the ear, and the leaves outperformed all the other statistical models explored with an <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:msup><mml:mrow><mml:mi>R<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:msup><\/mml:math> of 0.9893 and an RMSE of 0.0684\u2009mm in estimating the dry grain yield with wet wheat organ mass as the predictors. This proposed system can be applied as nondestructive in a field technique for wheat phenotyping. Additionally, it can be applied to other similar crops.<\/jats:p>","DOI":"10.1155\/2020\/1571936","type":"journal-article","created":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T23:30:22Z","timestamp":1599953422000},"page":"1-11","source":"Crossref","is-referenced-by-count":22,"title":["Wheat Grain Yield Estimation Based on Image Morphological Properties and Wheat Biomass"],"prefix":"10.1155","volume":"2020","author":[{"given":"Tchalla","family":"Korohou","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University\/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cedric","family":"Okinda","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University\/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, 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