Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform
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Authors
Yu, Neil
Li, Liujun
Schmitz, Nathan
Tian, Lei F.
Greenberg, Jonathan A.
Diers, Brian W.
Issue Date
2016
Type
Citation
Language
Keywords
Soybean , Breeding efficiency , UAV , Multispectral image , Object classification
Alternative Title
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 < 0.01). We developed a binary prediction model from time-course multispectral HTP image data and achieved over 93% accuracy in classifying soybean maturity. This prediction model was validated in an independent breeding trial with a different plot type. These results show that multispectral data collected from the UAV-based HTP platform could improve yield estimation accuracy and maturity recording efficiency in a modern soybean breeding program.
Description
Citation
Yu, N., Li, L., Schmitz, N., Tian, L. F., Greenberg, J. A., & Diers, B. W. (2016). Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sensing of Environment, 187, 91-101.
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In Copyright (All Rights Reserved)
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Issue
PubMed ID
ISSN
0034-4257