In order to solve the effects of environmental factors on the droplet settlement of a nutrient solution on plant roots when planting plants with ultrasonic aeroponic cultivation, this study aimed to obtain a suitable wind speed range and atomization time through a nutrient solution atomization experiment, to obtain the best control scheme through a multi-environmental parameter combination cultivation experiment. Taking an ultrasonic aeroponic cultivation device as the research object, and lettuce as the test material, experiments were carried out on two factors affecting the wind speed of an axial fan and the atomization time of the nutrient amount of ultrasonic aeroponic cultivation plants; the suitable wind speed range was 1.0–2.5 m/s. The temperatures of the lettuce root zones in the upper, middle, and lower layers of the ultrasonic aeroponic cultivation device at different time periods were obtained by atomizing the nutrient solution. When the optimum temperature for the root growth of lettuce was 15–20 °C and the wind speed was 1.0–2.5 m/s, the continuous atomization time of the nutrient solution was 66–184 min. Using a quadratic orthogonal rotation combination design method, three main factors, namely wind speed, ambient temperature, and atomization time, were selected to test droplet settlement in the lettuce roots. The droplet settlement in the lettuce root system was measured. The droplet settlement regression equation in the lettuce root system was established. The reliability of the regression model was tested according to the significance condition, and a simplified quadratic orthogonal regression equation was obtained. The main effect analysis, single factor analysis, and interaction effect analysis were used to analyze the model, and the model was further verified. The verification results showed that the relative error between the predicted value and the actual value of the average root droplet sedimentation was 5.8%. The optimum wind speed was 2.5 m/s, the ambient temperature was 16 °C, and the atomization time was 184 min when the ultrasonic aeroponic cultivation device designed in this study was used to cultivate lettuce. It could provide a theoretical reference and an experimental basis for the control of the related growth environment parameters of plants cultivated using ultrasonic aeroponic cultivation.

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.