During the process of jujube planting, there are not only natural risks caused by natural disasters but also market risks caused by price factors. In the study, firstly, wavelet analysis method was used to stabilize the jujube yield per unit area and the jujube price from 1997 to 2018 in Aksu region, Xinjiang, China. Secondly, EasyFit software was used to fit the distribution functions of yield per unit area and price, respectively. Thirdly, the optimal Copula function which connects the marginal distribution functions and its joint distribution function was selected with the principle of “the minimum square distance from the empirical Copula function.” Finally, taking the premium rate and the insurance amount as two decision variables, the farmer’s risk minimization as the objective function, around the four constraints of functions and role of insurance, the nonspeculative nature of insurance, the sustainability of insurance, and the moral hazard factors and the farmers’ willing to participate in insurance, the Copula-stochastic optimization model was set up to determine the premium rate of jujube revenue insurance in Aksu region.

Apple-picking robots are designed to accurately identify ripe apples, efficiently harvest them, and offer adaptable solutions applicable to various fruits. However, current methods show limited detection accuracy in complex environments due to challenges such as shading in natural settings, variations in ambient lighting, and diversity in fruit color, shape, and size. This study integrates Hue–Saturation–Value (HSV) color space transformation with YOLO v8 models to identify the quantity and location of apples, while also assessing their ripeness and weight. First, image data of apples in natural environments were collected and preprocessed using grayscale conversion and Gaussian filtering for denoising. Next, edge detection was performed using the Canny and Sobel algorithms, and apple counting was achieved via the YOLO v8 model. Ripeness was evaluated based on HSV and Red–Green–Blue (RGB) values, while weight estimation was conducted by constructing 3D models of the fruits. Finally, through color feature extraction and YOLO v8, apples were precisely identified among various fruits. Experimental results show that the YOLO v8 model achieved an average precision of 95% and an F1-score of 93.45%. Compared to existing algorithms such as Visual Geometry Group (VGG) and Residual Network (ResNet), YOLO v8 improved Mean Average Precision (mAP) by 0.14% and 8%, respectively. These advancements provide strong technical support for apple-picking robots, enabling faster and more accurate operations, improving fruit quality, and meeting the practical demands of agricultural production.

The design issues of special clothing against high temperature operation was studied. In accordance with the relationship of temperature difference between layers, using the relevant knowledge of heat transfer, heat conduction and the law of energy conservation, the heat relation model of each layer per unit area was established, and the temperature distribution of each layer of fabric materials was obtained, and the temperature distribution results of three layers of fabric materials were analyzed and verified. By using MATLAB software, the four parameter exponential model of skin temperature was obtained. The model can also be extend to the study of the optimal selection of insulation materials.