Improving potato yield prediction by combining cultivar information and uav remote sensing data using machine learning

Dan Li, Yuxin Miao, Sanjay K. Gupta, Carl J. Rosen, Fei Yuan, Chongyang Wang, Li Wang, Yanbo Huang

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was in-corporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

Original languageEnglish (US)
Article number3322
JournalRemote Sensing
Issue number16
StatePublished - Aug 22 2021

Bibliographical note

Funding Information:
The study was supported by the Northern Plains Potato Grower Association (NPPGA)/ Minnesota Area II Potato Research and Promotion Council; USDA Agricultural Research Service; Minnesota Department of Agriculture through the Crop Research Grant, GDAS? Project of Science and Technology Development (grant number: 2018GDASCX-0101); Guangdong Province Agricultural Science and Technology Innovation and Promotion Project (number 2021KJ102,2020KJ102, and 2019KJ102); and USDA National Institute of Food and Agriculture (State project 1016571). We would like to express our appreciation to Craig Poling and Bryan Poling at Sentek Systems for collecting the UAV remote sensing images and for preprocessing and mosaicking the images. We also would like to thank Matthew McNearney for managing the field experiments and for the plant sampling and yield determination.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Growing degree days
  • Growth stage
  • Potato marketable yield
  • Random forest
  • Support vector regression


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