Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making, which are also important requirements for precise agricultural development. Given the development of hyperspectral imaging technology, a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral images. In this study, the Unmanned Aerial Vehicle (UAV) hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were collected. The winter wheat yield prediction model was established by optimizing Vegetation Indices (VIs) feature scales and sample scales, incorporating Partial Least Squares Regression (PLSR), Random Forest algorithm (RF), and Back Propagation Neural Network algorithm (BPN). Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy (RMSE=948.88 kg/hm²). Contrary to the belief that more input features result in higher accuracy, PLSR, RF, and BPN models performed best when trained with the top 3, 8, and 4 VIs with the highest correlation, respectively. With an increase in training samples, model accuracy improves, reaching stability when the training samples reach 70. Using PLSR and optimal feature scales, UAV yield prediction maps were generated, holding significant value for field management in precision agriculture.

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.