Plant-level prediction of potato yield using machine learning and unmanned aerial vehicle (UAV) multispectral imagery

• Published in: Discover applied sciences Vol. 6; no. 12; pp. 649 - 11
• Main Authors: Tatsumi, Kenichi, Usami, Tamano
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 29.11.2024; Springer Nature B.V; Springer
• Subjects: Accuracy; Agricultural production; Algorithms; Applied and Technical Physics; Biomass; Calibration; Chemistry/Food Science; Crop yield; Data collection; Earth Sciences; Engineering; Environment; Feature selection; Growing season; Harvest; High resolution; Image enhancement; Image resolution; Learning algorithms; Machine learning; Materials Science; Optimization; Parameter identification; Plant-level yield; Plants; Plants (botany); Potassium; Potato yield; Potatoes; Precision agriculture; Predictions; Random variables; Regression analysis; Remote sensing; Sensors; Statistical analysis; Statistical models; Support vector machines; Unmanned aerial vehicle; Unmanned aerial vehicles; Variables; Vegetables; Vegetation; Vegetation index; United States > US; China; Feature selection; Plant-level yield; Unmanned aerial vehicle; Optimization; Potato yield; Machine learning
• ISSN: 3004-9261; 2523-3963; 3004-9261; 2523-3971
• DOI: 10.1007/s42452-024-06362-7

This study presents a new method for predicting the underground yield of potato at the plant level, using two key approaches: (1) identifying the critical variables for yield prediction based on plant height and vegetation index (VI) maps derived from unmanned aerial vehicle (UAV) imagery; (2) evalu...