Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Abstract

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

“…The UAV-based phenotyping is an emerging technique in practical crop breeding. Previous studies have shown that the UAV-based features and the machine learning model can be used together to predict crop yields in breeding work with a large number of crop genotypes ( Osval et al, 2017 ; Fei et al, 2021 ). In this study, ENR is a relatively new machine learning algorithm being used for yield prediction.…”

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

“…Winter wheat is one of the three major cultivated cereals and is the most widely-grown cereal crop in the world [1]. Wheat plays a crucial role in global food production, trade, and food security [2]. Estimating wheat yield prior to harvest on a large scale not only offers a scientific foundation for local governments to establish production goals, but also ensures food security [3].…”

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

“…For example, Qiao et al (2021) reported the efficiency of using long-time series multi-spectral images for yield mapping of different crop species using an automated spatial-spectral feature extractor. The application of hyperspectral reflectance in predicting the wheat grain yield was studied by Fei et al (2021) , who reported the effectiveness of red and NIR regions in predicting the grain yield in different irrigation regimes. The use of hyperspectral reflectance in predicting yield was not limited to agronomy crops and used for vegetables ( Awika et al, 2021 ), trees ( Ali and Imran, 2021 ), and industrial plants ( Holmes et al, 2020 ).…”