Trunk recognition is a critical technology for Camellia oleifera fruit harvesting robots, as it enables accurate and efficient detection and localization of vibration or picking points in unstructured natural environments. Traditional trunk detection methods heavily rely on the visual judgment of robot operators, resulting in significant errors and incorrect vibration point identification. In this paper, we propose a new method based on an improved YOLOv7 network for Camellia oleifera trunk detection. Firstly, we integrate an attention mechanism into the backbone and head layers of YOLOv7, enhancing feature extraction for trunks and enabling the network to focus on relevant target objects. Secondly, we design a weighted confidence loss function based on Facol-EIoU to replace the original loss function in the improved YOLOv7 network. This modification aims to enhance the detection performance specifically for Camellia oleifera trunks. Finally, trunk detection experiments and comparative analyses were conducted with YOLOv3, YOLOv4, YOLOv5, YOLOv7 and improved YOLOv7 models. The experimental results demonstrate that our proposed method achieves an mAP of 89.2%, Recall Rate of 0.94, F1 score of 0.87 and Average Detection Speed of 0.018s/pic that surpass those of YOLOv3, YOLOv4, YOLOv5 and YOLOv7 models. The improved YOLOv7 model exhibits excellent trunk detection accuracy, enabling Camellia oleifera fruit harvesting robots to effectively detect trunks in unstructured orchards.

Introduction: In the field of agriculture, automated harvesting of Camellia oleifera fruit has become an important research area. However, accurately detecting Camellia oleifera fruit in a natural environment is a challenging task. The task of accurately detecting Camellia oleifera fruit in natural environments is complex due to factors such as shadows, which can impede the performance of traditional detection techniques, highlighting the need for more robust methods.Methods: To overcome these challenges, we propose an efficient deep learning method called YOLO-CFruit, which is specifically designed to accurately detect Camellia oleifera fruits in challenging natural environments. First, we collected images of Camellia oleifera fruits and created a dataset, and then used a data enhancement method to further enhance the diversity of the dataset. Our YOLO-CFruit model combines a CBAM module for identifying regions of interest in landscapes with Camellia oleifera fruit and a CSP module with Transformer for capturing global information. In addition, we improve YOLOCFruit by replacing the CIoU Loss with the EIoU Loss in the original YOLOv5.Results: By testing the training network, we find that the method performs well, achieving an average precision of 98.2%, a recall of 94.5%, an accuracy of 98%, an F1 score of 96.2, and a frame rate of 19.02 ms. The experimental results show that our method improves the average precision by 1.2% and achieves the highest accuracy and higher F1 score among all state-of-the-art networks compared to the conventional YOLOv5s network.Discussion: The robust performance of YOLO-CFruit under different real-world conditions, including different light and shading scenarios, signifies its high reliability and lays a solid foundation for the development of automated picking devices.

A biomass pellet rotary burner was chosen as the research object, in order to study the influence of excess air coefficient on the combustion phenomenon of biomass rotary burner, the finite element simulation model of a biomass rotary burner was established, and simulation results of a biomass rotary burner were verified by the experiment. The computational fluid dynamics software was applied to simulate the combustion characteristics of biomass rotary burner in a steady-state condition, and the effect of excess air coefficient on temperature field and component concentration field in biomass rotary burner was analyzed. The results show that the flue gas flow rate inside the burner gradually increases with the increase of air velocity, the area with large temperature is mainly concentrated in the middle region of the rotary burner, and the maximum combustion temperature also appeared in the middle region of the combustion chamber, and the formation area of CO decreases with the increase of excess air coefficient. CO₂ is mainly concentrated in the middle region of the burner, and the CO₂ generating region decreases with the increase of excess air coefficient. The experimental value of the combustion temperature of the biomass rotary burner is in good agreement with the simulation results.

The Astragalus membranaceus (AM) slices were dried using three infrared drying techniques, including near infrared drying (NIR), mid infrared drying (MIR), and far infrared drying (FIR). Studies and discussions about the impacts of infrared temperature, power, slice thickness and wavelength on the drying properties, energy consumption, and associated performance were conducted. Results showed that the drying rate increased with increasing infrared temperature and wavelength, and with decreasing slice thickness, whereas the drying process was not significantly influenced by the infrared power. The drying efficiency of FIR was superior to that of NIR and with the increase of temperature, this improvement became more obvious. FIR drying was the fastest with the least energy consumption among the three drying methods. Rehydration ratio and color change of AM slices were proportional to infrared temperature and wavelength, but inversely proportional to slice thickness. The effective moisture diffusion coefficients of AM slices under NIR and FIR drying ranged from 0.49244×10⁻⁹ to 1.16352×10⁻⁹ m²/s and 1.00432×10⁻⁹ to 2.51135×10⁻⁹ m²/s, respectively.

The collection of Camellia oleifera flowers is a key foundation of Camellia oleifera flower pollen extraction. Due to the current problems of low efficiency, high labor intensity and the high cost of manual collection of Camellia oleifera flowers, a harvesting actuator was designed. By analyzing the inherent characteristics of the Camellia oleifera flower and the harvesting method, a harvesting structure using a combination of friction roller twisting harvesting and pipeline pneumatic conveying was designed. The geometric model of the Camellia oleifera flower was established and the motion analysis of the flower was carried out, which indicated that the Camellia oleifera flower would tend to a stable state for easy picking after entering the actuator. Using Automatic Dynamic Analysis of Mechanical Systems 2015 software (ADAMS, MSC. Software Corporation, Santa Ana, CA, USA) to simulate the process of Camellia oleifera flower picking, a mechanical analysis was performed in the contact plane to prove the theoretical feasibility of friction roller picking these flowers, and the main influencing factor was obtained as the speed of the friction roller. The test prototype for Camellia oleifera flower picking was built, and the picking experiment was implemented to study the effect of motor speed on the picking time of single Camellia oleifera flowers and the effect of the success rate of the flower picking. The test results show that when the motor speed is 400 r/min, the picking success rate is 96%, the picking time of a single flower is 1.2 s, and the speed of the machine collection of Camellia oleifera flowers is 2.3 times that of manual collection, which proves the realistic feasibility of this picking actuator. This paper provides an important reference and basis for the research and development of a flower harvesting actuator.