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.

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 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.

Background: Wheat is an important food crop globally, and timely prediction of wheat yield in breeding efforts can improve selection efficiency. Traditional yield prediction method based on secondary traits is time-consuming, costly, and destructive. It is urgent to develop innovative methods to improve selection efficiency and accelerate genetic gains in the breeding cycle. Results: Crop yield prediction using remote sensing has gained popularity in recent years. This paper proposed a novel ensemble feature selection (EFS) method to improve yield prediction from hyperspectral data. For this, 207 wheat cultivars and breeding lines were grown under full and limited irrigation treatments respectively, and their canopy hyperspectral reflectance was measured at the flowering, early grain filling (EGF), mid grain filling (MGF), and late grain filling (LGF) stages. Then, 115 vegetation indices were extracted from the hyperspectral reflectance and combined with four feature selection methods, i.e., mean decrease impurity (MDI), Boruta, FeaLect, and RReliefF to train deep neural network (DNN) models for yield prediction. Next, a learning framework was developed by combining the predicted values of the selected and the full features using multiple linear regression (MLR). The results show that the selected features contributed to higher yield prediction accuracy than the full features, and the MDI method performed well across growth stages, with a mean R² ranging from 0.634 to 0.666 (mean RMSE = 0.926–0.967 t ha⁻¹). Also, the proposed EFS method outperformed all the individual feature selection methods across growth stages, with a mean R² ranging from 0.648 to 0.679 (mean RMSE = 0.911–0.950 t ha⁻¹). Conclusions: The proposed EFS method can improve grain yield prediction from hyperspectral data and can be used to assist wheat breeders in earlier decision-making.

Timely and accurate monitoring of winter wheat yields is beneficial for the macro-guidance of agricultural production and for making precise management decisions throughout the winter wheat reproductive period. The accuracy of crop yield prediction can be improved by combining unmanned aerial vehicle (UAV)-based multispectral data with deep learning algorithms. In this study, 16 yield-sensitive vegetation indices were constructed, and their correlations were analyzed based on UAV multispectral data of winter wheat at the heading, flowering, and filling stages. Seven input variable sets were obtained based on the combination of data from these three periods, and four generalized machine learning algorithms (Random Forest (RF), K-Nearest Neighbor (KNN), Bagging, and Gradient Boosting Regression (GBR)) and one deep learning algorithm (1D Convolutional Neural Network (1D-CNN)) were used to predict winter wheat yield. The results showed that the RF model had the best prediction performance among the generalised machine learning models. The CNN model achieved the best prediction accuracy based on all seven sets of input variables. Generalised machine learning models tended to underestimate or overestimate yields under different irrigation treatments, with good prediction performance for observed yields < 7.745 t·ha⁻¹. The CNN model showed the best prediction performance based on most input variable groups across the range of observed yields. Most of the differences between observed and predicted values (Y_i) for the CNN models were distributed between −0.1 t·ha⁻¹ and 0.1 t·ha⁻¹, and the model was relatively stable. Therefore, the CNN model is recommended in this study for yield prediction and as a reference for future precision agriculture research.