Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery

Abstract Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cy...

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Autores principales:	Wenan Yuan, Nuwan Kumara Wijewardane, Shawn Jenkins, Geng Bai, Yufeng Ge, George L. Graef
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/20981ad85a1d4a869b8d3f32a73a9051
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id	oai:doaj.org-article:20981ad85a1d4a869b8d3f32a73a9051
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spelling	oai:doaj.org-article:20981ad85a1d4a869b8d3f32a73a90512021-12-02T15:08:20ZEarly Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery10.1038/s41598-019-50480-x2045-2322https://doaj.org/article/20981ad85a1d4a869b8d3f32a73a90512019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-50480-xhttps://doaj.org/toc/2045-2322Abstract Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R2 = 0.68), Maturity (RMSE = 3.70, R2 = 0.76) and Seed Size (RMSE = 1.63, R2 = 0.53) were identified as potential soybean traits that might be early predictable.Wenan YuanNuwan Kumara WijewardaneShawn JenkinsGeng BaiYufeng GeGeorge L. GraefNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-17 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenan Yuan Nuwan Kumara Wijewardane Shawn Jenkins Geng Bai Yufeng Ge George L. Graef Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
description	Abstract Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R2 = 0.68), Maturity (RMSE = 3.70, R2 = 0.76) and Seed Size (RMSE = 1.63, R2 = 0.53) were identified as potential soybean traits that might be early predictable.
format	article
author	Wenan Yuan Nuwan Kumara Wijewardane Shawn Jenkins Geng Bai Yufeng Ge George L. Graef
author_facet	Wenan Yuan Nuwan Kumara Wijewardane Shawn Jenkins Geng Bai Yufeng Ge George L. Graef
author_sort	Wenan Yuan
title	Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
title_short	Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
title_full	Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
title_fullStr	Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
title_full_unstemmed	Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
title_sort	early prediction of soybean traits through color and texture features of canopy rgb imagery
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/20981ad85a1d4a869b8d3f32a73a9051
work_keys_str_mv	AT wenanyuan earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery AT nuwankumarawijewardane earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery AT shawnjenkins earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery AT gengbai earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery AT yufengge earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery AT georgelgraef earlypredictionofsoybeantraitsthroughcolorandtexturefeaturesofcanopyrgbimagery
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Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery

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