The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

Kai-Yun Li, Niall Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Ming-Der Yang, Janar Raet, Ants Vain, Are Selge, Kalev Sepp

Research output: Contribution to journalArticlepeer-review


A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed
the best performance for ANN in the 11-day before harvest category (R²=0.90, NRMSE=0.12), followed by RFR (R²=0.90 NRMSE =0.15), and SVR (R²=0.86, NRMSE=0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well
as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage.
Original languageEnglish
Article number1994
Number of pages24
JournalRemote Sensing
Issue number10
Publication statusPublished - 19 May 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding Information: This research was funded by the European Regional Development Fund within the Estonian National Programme for Addressing Socio-Economic Challenges through R&D (RITA): L180283PKKK and the Doctoral School of Earth Sciences and Ecology, financed by the European Union, European Regional Development Fund (Estonian University of Life Sciences ASTRA project ?Value-chain based bio-economy?). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • unmanned aerial system
  • red clover
  • Random Forests
  • support vector regression
  • Artificial Neural Network (ANN)
  • tillage
  • fertilizing
  • mature
  • forage legume
  • yield estimation
  • Manure
  • Random forest
  • Yield estimation
  • Fertilizing
  • Red clover
  • Tillage
  • Unmanned aerial system
  • Support vector regression
  • Artificial neural network
  • Forage legume


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