In order to investigate the cutting mechanical characteristics of Caragana Korshinskii (C.K.) branches and explore the optimal combination of cutting parameters to support the subsequent equipment development, this paper explores the relationship between branch diameter D, average cutting speed v, wedge angle β, slip cutting angle α, cutting height h, cutting gap t, moisture content M and peak cutting force by using a homemade swing-cut branch cutting test bench with peak cutting force of branches as the target value under unsupported and supported cutting methods, respectively, through single-factor tests. Based on the single-factor test, v, β, α and t were selected as the test factors, and a multi-factor test was conducted with the peak cutting force as the target. Test result: The best combination of unsupported cutting in the range of multi-factor test is v for 3.315 m·s−1, β for 20°, α for 20°, when the peak cutting force is 95.690 N. Supported cutting multi-factor test range to get the best combination of v for 3.36 m·s−1, β for 20°, α for 20°, t for 1.38 mm, when the peak cutting force is 53.082 N. The errors of the predicted peak cutting force and the measured peak cutting force of the obtained model were 1.3% and 3.9%, respectively, which prove that the cutting parameters were optimized reliably. This research can provide a theoretical basis for subsequent development the C.K. harvesting equipment.

This paper researches on methods of the color image segmentation method of Lingwu long jujubes based on the maximum entropy to achieve the accuracy of image segmentation and improve accuracy of machine recognition. According to law between the color of Lingwu long jujubes and characteristic of environment, starting from the hue information, this paper is first to explore the difference between the hue of Lingwu long jujubes and the environment which it lives and then use maximum entropy to segment image. It finds optimal threshold by mathematical criterion judging the accuracy of image segmentation. The method of pre-processing of image is mean filter firstly. Then, it extracts hue information of true color image and uses maximum entropy for image segmentation, judging accuracy of image segmentation by segmentation area whether it is in accordance with the 3σ principle. Mathematical morphology is used for smoothing image and eliminating small holes. Finally, segmented image will be obtained through labeling the image by using methods of labeled image and using characteristic parameters for extracting feature. By comparing the segmentation effect with artificial method of the 30 Lingwu long jujubes images, it proves that the color image segmentation method of Lingwu long jujubes based on the maximum entropy has good effect to extract the object region. The accuracy of segmentation rate is up to 89.60%. The time that the algorithm run is 1.3132 s.

To solve the problems of poor sawing surface quality, severe blade wear and high power consumption caused by unreasonable working parameters in the process of Caragana korshinskii (C.K.) stumping, this study explored the effects of branch diameter (D), sawing speed (vc), feeding speed (vf), cutting inclination (α), number of circular saw teeth (T) and moisture content (M) on sawing power consumption (P) and sawing surface quality (A) through a single-factor test using a homemade branch sawing bench. Based on the Box–Behnken design principle, a multi-factor test was carried out based on a single-factor test with vc, vf, α and T as influencing factors and with P and A as targets, establishing a regression model. The test results show that the sawing power consumption (P) increases with increasing D, decreases with increasing M, and decreases first and then increases with increasing vc, vf, α and T; the sawing surface quality (A) increases first and then decreases with increasing D, increases with increasing M, and first increases and then decreases with increasing vc, vf, α and T. The optimum combination of parameters for the regression model was obtained with vc of 45.24 m/s, vf of 0.34 m/s, α of 10° and T of 100, which resulted in the P of 177.46 J and A of 85.87%. The errors between the predicted and actual values of P and A are 3.1% and 6%, respectively. The study can provide information to support the development of subsequent C.K. stubble equipment.