NDLI: Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean

Content Provider	Springer Nature : BioMed Central
Author	Herrero-Huerta, Monica Rodriguez-Gonzalvez, Pablo Rainey, Katy M.
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.
Related Links	https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-020-00620-6.pdf
Ending Page	16
Page Count	16
Starting Page	1
File Format	HTM / HTML
ISSN	17464811
DOI	10.1186/s13007-020-00620-6
Journal	Plant Methods
Issue Number	1
Volume Number	16
Language	English
Publisher	BioMed Central
Publisher Date	2020-06-01
Access Restriction	Open
Subject Keyword	Plant Sciences Biological Techniques Unmanned aircraft system (UAS) High throughput phenotyping Soybean Structure from Motion (SfM) Machine learning (ML) Yield Point clouds
Content Type	Text
Resource Type	Article
Subject	Plant Science Biotechnology Genetics
Journal Impact Factor	4.7/2023
5-Year Journal Impact Factor	5.6/2023

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