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

Abstract

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

Wheat Yield Prediction with Machine Learning based on MODIS and Landsat NDVI Data at Field Scale

“…In the study based on crop development periods, a good prediction accuracy was obtained with the SVM model. Obtaining results at similar levels in previous studies supported the argument that the SVM model is one of the appropriate methods for estimating crop yield(Joshi et al, 2021;Ju et al 2021;Abebe et al, 2022;Li et al, 2022).Fig. 8 Model performances by years in producer lands…”

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods

“…The application of UAV platforms makes it possible to obtain and analyze tobacco plants quickly at the canopy level ( Inoue et al., 2012 ; Zhu et al., 2020 ; Liu et al., 2021 ). In addition, with the improvement in load capacity and battery endurance, there is also significant performance in face of large-scale regional observation tasks ( Li et al., 2022 ). Compared to handheld spectrometers, UAV platforms save a lot of manual work and time; and compared to satellite platforms, UAV platforms are relatively accurate and convenient observation tools.…”

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

“…3) Hybrid RL algorithms: a) Model-ensemble based: Model ensemble learning is a technique for developing a single learning model capable of making inferences on supplied data by combining different learning models, such as Logistic Regression and Naive Bayes classifiers. To predict wheat output using UAVs in the winter, Li et al [68] utilized an ensemble-based learning and hyperspectral-based approach, with the latter being used to assess crop attributes due to the fact that hyperspectral data may give rich spectral information. Boruta feature selection and the Pearson correlation coefficient (PCC) are two examples of the kinds of feature selection techniques used to filter out data with unusually high spectral indices.…”