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This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7) to address the low accuracy of apple fruit target recognition caused by high fruit density, occlusion, and overlapping issues. Firstly, we proposed a preprocessing algorithm for the split image with overlapping to improve the robotic intelligent picking recognition accuracy. Then, we divided the training, validation, and test sets. Secondly, the MobileOne module was introduced into the backbone network of YOLOv7 to achieve parametric fusion and reduce network computation. Afterward, we improved the SPPCSPS module and changed the serial channel to the parallel channel to enhance the speed of image feature fusion. We added an auxiliary detection head to the head structure. Finally, we conducted fruit target recognition based on model validation and tests. The results showed that the accuracy of the improved YOLOv7 algorithm increased by 6.9%. The recall rate increased by 10%, the mAP1 algorithm increased by 5%, and the mAP2 algorithm increased by 3.8%. The accuracy of the improved YOLOv7 algorithm was 3.5%, 14%, 9.1%, and 6.5% higher than that of other control YOLO algorithms, verifying that the improved YOLOv7 algorithm could significantly improve the fruit target recognition in high-density fruits.

The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot.

Simulation analysis and parameter optimization are performed for the loading and mixing devices of a self-propelled total mixed ration mixer. To reveal the three-dimensional movement of silage material under the action of the loading cutter roller, the latter is modeled using SolidWorks software. ANSYS/LS-DYNA software is used to simulate the process of silage cutting, which is modeled using smoothed particle hydrodynamics coupled with the finite element method. e cutting force and power consumption are simulated, and the behavior of the equivalent strain of the silage is determined. e results showed that silage was broken up mainly by extrusion and shear force due to the loading cutter roller. e power consumption according to the simulation is consistent with the value from an empirical formula, confirming the validity of the proposed modeling method. To study the mixing performance and obtain the optimum parameters of the mixing device, the Hertz-Mindlin model is used for the interaction between material particles and mixing device. A three-factor, five-level method is used to optimize the mixing performance. Material-mixing time, loading rate, and auger speed are chosen as experimental factors and mixed uniformity as an evaluation index. It is found that auger speed and material mixing time have significant effects on mixing uniformity. ese results provide reference values allowing the analysis of the crushing of silage and selection of the optimum parameters for mixing performance.