Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Abstract

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 incorporated (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.

A review of remote sensing for potato traits characterization in precision agriculture

“…Potato tuber yield is jointly affected by genetic variation, environmental conditions (soil and weather), seed quality, and crop management practices ( Li et al, 2021a ). Classical potato yield prediction models are often used to estimate yields within the growing season by considering nitrogen fertilizer, temperature, and daylight or the incidence of solar radiation ( Wang X. et al, 2021 ).…”

“…Unmanned aerial vehicle-based imagery is largely used for predicting potato yield in site-specific studies. Multi- ( Tanabe et al, 2019 ; Li et al, 2021a ) and hyper-spectral ( Li B. et al, 2020 ; Sun et al, 2020 ) have been used to establish and validate a variety of machine learning models for predicting potato yield. The prediction performance of images collected at different growth stages was compared.…”

“…The prediction performance of images collected at different growth stages was compared. It was found that images at earlier growth stages, i.e., the tuber initiation stage ( Li et al, 2021a ) and vegetative growth stage ( Tanabe et al, 2019 ), tuber bulking stage ( Li B. et al, 2020 ) had better prediction performances than those at the later stage when close to the end of seasons. Even though, Sun et al (2020) reported a decent prediction accuracy with R 2 = 0.63, RMSE = 3.03 ton/ha, and RMSE% = 5.8 when using hyperspectral image features at tuber maturation stage and machine learning models to predict potato tuber yield under four irrigation levels.…”

Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers

“…Aspects related to the variability of these factors may pose a particular problem [7]. Many researchers have dealt with this issue with varying degrees of success [8,9,10].…”

“…Li et al [10] found that accurate, high-resolution yield maps are needed to identify spatial patterns of yield variability, to identify key factors influencing yield variability, and to provide detailed management information in precision farming. Varietal differences may significantly affect the forecasting of potato tuber yields with the use of remote sensing technologies.…”