RT info:eu-repo/semantics/article
T1 Yield prediction by machine learning from UAS‑based mulit‑sensor data fusion in soybean
A1 Herrero-Huerta, Mónica
A1 Rodríguez Gonzálvez, Pablo
A1 Rainey, Katy M.
A2 Ingeniería CartograficaGeodesica y Fotogrametria
K1 Ingenierías
K1 Unmanned aircraft system (UAS)
K1 High throughput phenotyping,
K1 Soybean
K1 Structure from Motion (SfM)
K1 Machine learning (ML)
K1 Yield
K1 Point clouds
AB 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.
SN 1746-4811
LK http://hdl.handle.net/10612/14771
UL http://hdl.handle.net/10612/14771
NO 16 p.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 16-may-2024