Dataset Information

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 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.

SUBMITTER: Yuan W

PROVIDER: S-EPMC6773688 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Yuan Wenan W Wijewardane Nuwan Kumara NK Jenkins Shawn S Bai Geng G Ge Yufeng Y Graef George L GL

Scientific reports 20191001 1

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 expl ...[more]

PMID: 31575995

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Dataset Information

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

Publications

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

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