Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean

Monica Herrero-Huerta; Pablo Rodriguez-Gonzalvez; Katy M Rainey

doi:10.1186/s13007-020-00620-6

Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean

Plant Methods. 2020 Jun 1:16:78. doi: 10.1186/s13007-020-00620-6. eCollection 2020.

Authors

Monica Herrero-Huerta¹, Pablo Rodriguez-Gonzalvez², Katy M Rainey¹

Affiliations

¹ Department of Agronomy, Purdue University, West Lafayette, IN 47906 USA.
² Department of Mining Technology, Topography and Structures, Universidad de Leon, Ponferrada, Spain.

Abstract

Background: Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.

Results: Algorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost.

Conclusions: Results provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.

Keywords: High throughput phenotyping; Machine learning (ML); Point clouds; Soybean; Structure from Motion (SfM); Unmanned aircraft system (UAS); Yield.