The harvesting straw feed crops (silage corn, alfalfa, herbaceous mulberry, etc.) was tedious, high-labor-cost, and large-nutrient-loss. A self-propelled straw forage crop harvester, which could realize the integration of cutting, flattening and modulating, chopping, and throwing straw forage crops, was designed. The cutting angle could freely be adjusted between 0° and 8°. The max rotation speed of the flattening roller could reach 590 r/min and could be adjusted consecutively by the hydraulic control device. To verify the performance of this machine, several harvesting experiments of alfalfa, silage corn, and herbaceous mulberry with different moisture, were carried out on this machine. During the experiment, the average working speed of the machine was 1.6 m/s, the cutting height was 40–80 mm, and the flattening rate was 97.14%. It is determined that the suitable cutting speed for harvesting alfalfa is 2131 r/min; the suitable cutting speed for harvesting silage corn is 836 r/min; the suitable cutting speed for harvesting herb mulberry is 1045 r/min. The design of the machine can not only improve labor productivity and reduce the nutrient loss of forage crop but also support the silage harvesting machinery and equipment for forage crop.

The working environment of agricultural knives is bad, which makes the knives wear out easily. A wear resistant layer of AlCoCrFeNi high entropy alloy (HEA) reinforced by tungsten carbide (WC) was prepared by laser cladding on one side of the cutting edge of a 65 Mn silage knife. Both the effects of WC addition on the microstructure and mechanical properties of AlCoCrFeNi (WC)_x (x = 0, 0.1, 0.2 and 0.3 in mass percentage) alloys were investigated. All experimental alloys displayed a crystalline structure of simple body centered cubic (BCC). The hardness of the cladding layer increases with the increase of WC content, and the hardness value enhances from 740 HV0.2 to 1060 HV0.2. A self-grinding edge was formed during working for the cladded knives. The cutting quality can be improved and the service life of agricultural knives can be increased meanwhile. The weight loss rate of untreated knives was about 2.64 times that of the cladded knives after a 76 h field experiment.

The working environment of agricultural cutting tools is poor, and the operational quality and efficiency are reduced after they become blunt. This study aimed to develop a high wear-resistant agriculture knife with a long life. A Ni–WC alloy, wear-resistant layer was prepared using laser cladding technology on one side of the cutting edge of a 65 Mn silage knife. A self-grinding edge was formed when the cladded knife was used, which improved the cutting quality and service life of the knife. The microstructure, phase, composition, and hardness distribution of the cladding layer were detected and analyzed. The impact toughness and wear resistance of the laser-cladded samples were analyzed, and the cladded knife was tested in the field. The results show that a cladded layer with a dense microstructure formed metallurgical bonds with the substrate. The microhardness was uniform across the cladded layer, and the average hardness of the micro Vickers was approximately 1000 HV(0.2), which was approximately three times the hardness of the substrate. The impact toughness and wear resistance of the coated knife were obviously higher than those of uncoated knives. The field tests showed that compared with a conventional 65 Mn knife, the self-grinding knife with laser cladding could maintain its sharp cutting shape after operation for 76 h, which greatly extended the service life of the knife. This study improved the service life of an agricultural cutting tool, which enhanced the cutting performance and efficiency at the same time.

The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms.

The classification of silkworm cocoons is essential prior to silk reeling and serves as a key step in improving the quality of raw silk. At present, cocoon classification mainly relies on manual sorting, which is labor-intensive and inefficient. In this paper, a cocoon detection algorithm S-YOLOv8_c based on the cooperation of MobileSAM and YOLOv8 for the mountage cocoons was proposed. The MobileSAM with a designed area thresholding algorithm was used for the semantic segmentation of mountage cocoon images, which could mitigate the effect of complex backgrounds and maximize the discriminability of cocoon features. Subsequently, the BiFPN was added to the neck of YOLOv8 to improve the multiscale feature fusion capability. The loss function was replaced with the WIoU, and a dynamic non-monotonic focusing mechanism was introduced to improve the generalization ability. In addition, the GAM was incorporated into the head to focus on detailed cocoon information. Finally, the S-YOLOv8_c achieved a good detection accuracy on the test set, with a mAP of 95.8%. Furthermore, to experimentally validate the sorting ability, we deployed the proposed model onto the self-developed Cartesian coordinate automatic cocoon harvester, which indicated that it would effectively meet the requirements of accurate and efficient cocoon sorting.

When seeds are treated with a high-voltage electric field (HVEF) to improve seed vigor, due to the large differences in the biological electromagnetic effects on different types of seeds, the methods of variance analysis and regression analysis based on data statistics are generally used to construct the optimal electric field dose prediction model; however, the generalization performance of the prediction model tends to be poor. To solve this problem, the electric intensity, frequency and treatment time were taken as the input variables for hybrid support vector regression (SVR) analysis to establish the prediction model of the seed comprehensive germination index. The whale optimization algorithm (WOA) was used to optimize the kernel parameters of the SVR. The optimized hybrid WOA–SVR model predicted the optimal comprehensive germination index of aged cotton (Gossypium spp.) seeds to be 329, the optimal HVEF dosage was 3.64 kV/cm × 99 s, and the frequency was 1.4 Hz. The aged cotton seeds were treated with the optimal HVEF and the germination test was carried out. Compared with the check (CK), the comprehensive germination index of seeds increased by 48%. The research results provided a new method and new idea for the optimal design of parameters for seed treatment with HVEF.