In view of the problems that the fuselage inclines and the driving straightness is difficult to guarantee due to the sinking and sliding of the wheels when the high-clearance plant protection machine is working in the paddy field, this paper takes high-clearance wheels as the research object, based on the paddy field driving environment, establishes a prediction model of the wheel subsidence through derivation, and explores the influence of different wheel parameters on the subsidence characteristics through experiments, so as to improve the chassis trafficability. At the same time, using the test data under different wheel parameters, the prediction model of the settlement of the working chassis with high clearance is correspondingly modified. Finally, the paddy field trafficability of the working chassis is compared and analyzed based on different tire parameters. The results show that when the wheel slip rate is 0.5, the traction force of the solid tire is 37% higher than that of the pneumatic tire; when the height of the wheel spike increases, the traction force increases, and the settlement decreases obviously; proper increase of the wheel diameter can improve the passing performance of the chassis; with the increase of the tire width, the angle of soil penetration decreases while the tire is driving, and the angle of the slope climbing increases; and when the load changes, the driving coefficient is proportional to the traction coefficient, and the tire resistance coefficient is inversely proportional to the traction coefficient. Through the research on the settlement mechanism of the high-clearance operation chassis and the analysis of the paddy field trafficability, the stability of the high-clearance plant protection machine in the paddy field has been improved, providing a platform and guarantee for subsequent precision operation.

The uniform and accurate mixing of pesticides in water is a necessary prerequisite for plant protection, especially for enabling precise variable spraying, and is also an important method to achieve a precise reduction in pesticide spraying. In order to ensure the uniform mixing of pesticides and water and solve the problems of traditional injection mixers, such as the limited range in the mixing ratio and unadjustable proportion, an active injection liquid mixer is designed in this paper. The mixer can be matched with an online mixing and spraying device to achieve accuracy in mixing and spraying. In this paper, a computational fluid dynamics (CFD) method is used to optimize the structure of the mixer. Through comparative analysis, the optimal structure of the mixer was found. It has a spherical head and conical tail, the number of guide plates is seven, and the shape is semicircular. By calculating the volume fraction of pesticide distribution under different cross-sections, the coefficient of variation in the process of mixing is obtained. The analysis shows that the maximum coefficient of variation of the ball-head cone-tail active injection mixer was 2.88% (lower than the allowable 5%) with a mixing ratio ranging from 300:1 to 3000:1. At the same time, image analysis methods of high-definition photography and ultraviolet spectrophotometry were used to analyze the mixing effect of the mixer. The test results show that, when the pressure of the pesticide injection is 1 MPa, the distribution of the pesticide and water in the ball-head cone-tail injection mixer is more uniform under different mixing ratios, and it has a better spatio-temporal distribution uniformity with the concentration changing a little at different times and different spatial locations. The mixer can provide a theoretical reference and technical support for the subsequent realization of an accurate online variable spray.

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jjocs costly. At the same time, the residual chemical detection reagents will also pollute the ecological environment. Therefore, it is of great significance to find an efficient and convenient nondestructive detection method for fatty acid content of camellia seed. Currently, hyperspectral technology has been widely used in food quality detection 3) , food safety detection 4,5) , fruit damage and internal components detection 6) , and vegetable freshness detection 7) . Using various spectral equipment to detect food related components and contents has the advantages of high efficiency and convenience, and has been widely used in the field of food detection. For example, Galtier et al. 8) established qualitative models of different producing areas of French virgin olive oil and quantitative models of various fatty Abstract: As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R 2 ) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (R C 2 ) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (R P 2 ) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.

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