Nowadays, with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology, people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images. The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images. In this study, the geometric correction accuracy under various geometric correction models (including affine transformation model, local triangulation model, polynomial model, direct linear transformation model, and rational function model) and resampling methods (including nearest neighbor resampling method, bilinear interpolation resampling method, and cubic convolution resampling method) were analyzed. Furthermore, the distribution, number, and accuracy of control points were analyzed based on the control variable method, and precise ground control points (GCPs) were analyzed. The results showed that the average geometric positioning error of UAV hyperspectral images (at 80 m altitude AGL) without geometric correction was as high as 3.4041 m (about 65 pixels). The optimal geometric correction model and parameter combination of the UAV hyperspectral image (at 80 m altitude AGL) used a local triangulation model, adopted a bilinear interpolation resampling method, and selected 12 edge-middle distributed GCPs. The correction accuracy could reach 0.0493 m (less than one pixel). This study provides a reference for the geometric correction of UAV hyperspectral images.

The threshing mixture of Cyperus esculentus (Tiger nut) planted in sandy areas is complex during harvesting, and there are problems of high impurity rate and high cleaning loss rate of the seeds. In this paper, the threshing mixture of Cyperus esculentus is taken as the research object, and the suspension speed of each component is measured to design an “airflow + vibration” sieving device. Use the vector polygon method to explore the law of motion of the vibrating sieve, establish the force model of the seeds on the sieve surface, and analyze the conditions for the seeds to penetrate the sieve. According to the principle of seeds penetrating the sieve and long stalks not penetrating the sieve, the geometric model of sieve blades opening degree adjustment mechanism is constructed. Study of the conditions under which long stalks can be thrown out of the machine. The experiment bench of the cleaning device was built, and the evaluation indexes were the rate of seed impurity and the rate of cleaning loss of the device, and the crank speed, sieve blades opening degree and fan speed were used as the experiment factors to study the influence law of each influencing factor on the evaluation index through single-factor experiment. Using response surface methodology to find the optimal combination of working parameters of the cleaning device. To verify the device’s application on the combine harvester to improve the effectiveness of the cleaning operation of the Cyperus esculentus combine harvester.

Few studies have been carried out on special shredding technology and equipment, hence it is hard to find out the characteristic rule of distribution of residual films after shredding. By evaluating the mechanical properties of the residual film during the cutting process of a mixture of mechanically recovered residual film and impurities, the main parameters influencing the film distribution feature were acquired. Physical tests were conducted on the basis of a multi-blade toothed shredding device. A relationship model between the film distribution feature and main parameters was constructed through the central grouping method and regression analysis of variance, aiming to investigate the influence rule of main parameters on the film distribution feature and the interaction between them, and to obtain the optimal combination of parameters for the cutting device. The difference between the experimental validation value and the model prediction ranged from 1.03% to 9.56%, which showed that the model has reliable and accurate predictive ability.