Neil Yu, Liujun Li, Nathan Schmitz
Dec 15, 2016
Citations
6
Influential Citations
141
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Quality indicators
Journal
Remote Sensing of Environment
Abstract
Abstract Advances in phenotyping technology are critical to ensure the genetic improvement of crops meet future global demands for food and fuel. Field-based phenotyping platforms are being evaluated for their ability to deliver the necessary throughput for large scale experiments and to provide an accurate depiction of trait performance in real-world environments. We developed a dual-camera high throughput phenotyping (HTP) platform on an unmanned aerial vehicle (UAV) and collected time course multispectral images for large scale soybean [Glycine max (L.) Merr.] breeding trials. We used a supervised machine learning model (Random Forest) to measure crop geometric features and obtained high correlations with final yield in breeding populations (r = 0.82). The traditional yield estimation model was significantly improved by incorporating plot row length as covariate (p