A ramie stripping machine has been commonly used in modern agriculture in recent years. However, the current manual feeding and back-pulling cannot fully meet the large-scale production, due to the high operational intensity, low safety, and unstable stripping quality. In this study, a double-drum stripping device was designed to replace the manual feeding and reverse-pulling in an automatic ramie fiber stripping machine. There were also the different mechanical properties between the ramie fiber layer and the woody part. Firstly, some measurements were performed on the physical dimensions and mechanical property parameters of "Chuanzhu No. 11" ramie stalks. Then, the technical solution of a double clamping mechanism was proposed with the synchronous belt clamping and conveying, motor-driven clamping mechanism flipping and changing position, and double-drum reverse stripping. The ramie stalks were fed directly, and then stripped at the base and tip of the ramie stalks in turn by the changing position of the clamps. The toothed rubber clamping plate was used to hold the stalks. The grooves and projections of the toothed rubber plate were firmly held to prevent the stalks crushed by the clamps and slipping out. The operation of the ramie stripping machine was divided into four steps: the straw feeding, base stripping, tip stripping, and collecting ramie skin. The key components mainly included the transposition clamping, lateral feeding, and ramie stripping device. The mechanical analysis of double-drum ramie stalk stripping showed that the stripping force was closely related to the drum speed. A mechanical test was carried out to investigate the relationship between the stalk feeding angle and stripping force. The results showed that the stalk feeding angle and stripping force shared a highly significant negative linear correlation. The structural design and theoretical analysis were performed on the main components in the ramie stripping device and clamping mechanism. After that, the structure and motion parameters of the ramie stripping machine were determined, including drum speed, reverse-pulling speed, and feeding angle. Furthermore, a single-factor simulation model was established using ANSYS/LS-DYNA to simulate the process of stripping and the amount of xylem removal, the loss of the bast fiber layer, and the force of the feeding direction. Previously, the ramie stalk actual stripping process was analyzed and a simulation model was developed. The structure of the ramie stalk was also analyzed during this time. The optimal ranges of parameters were achieved for a better stripping effectiveness: the roller speed of 350-650 r/min, the reverse-pulling speed of 0.2-0.4 m/s, and the feeding angle of 5°-15°. A three-factor and three-level orthogonal test was conducted, according to the Box-Behnken method. The results showed that the drum speed, reverse-pulling speed, and feeding angle posed the significant effects on the fiber percentage of fresh stalk, and the impurity rate of raw fiber using ANOVA and response surface analysis. In addition, a significant coupling effect was found in the interaction of drum speed and reverse-pulling speed, as well as each experimental factor, but there was no a simple linear relationship. The optimal operating parameters were obtained for the ramie stripping machine with the reverse-pulling clamping: the stripping drum speed of 450 r/min, the reverse-pulling speed of 0.32 m/s, and the feeding angle of 11°. Finally, the validation test of the ramie stripping machine prototype was carried out using the optimized parameters. A better performance was achieved in an average fresh stem fiber yield of 5.03% and an average impurity rate of 1.14% of raw ramie, fully meeting the national technical standards of the ramie stripping machine. The transposition clamping device can be expected to realize the ramie clamping and feeding, as well as the automatic reverse pulling. The simple and optimal structure of the ramie stripping machine was had improved the performance and quality of ramie stripping. The finding can provide a technical reference for the development of an automatic lightweight ramie stripping machine.

Pile picking is commonly used for green Sichuan pepper in southern China. Complex stacking scenarios can be formed by the pruned prickly ash branches, including branches, fruits, and leaves. Such stacking scenes have limited the high level of automation. However, the existing harvesters cannot fully meet the large-scale production during pile picking. It is often required to recognize and locate the prickly ash branches for the high grasping efficiency. In this study, a grasping sequence reasoning was proposed using an improved YOLOv8-Seg network. The network structure was optimized to enhance the perception and integration of the multi-scale features. Specifically, a convolutional block attention module (CBAM) was embedded before the feature concatenation in the C2f modules, corresponding to the P4 and P5 layers of the backbone network. The attention weights of feature maps were adjusted adaptively. Some features of the targets were integrated to strengthen at different spatial positions. Meanwhile, the original spatial pyramid pooling-fast (SPPF) module was replaced by an atrous spatial pyramid pooling (ASPP) module. The network was also reinforced to represent both local and global contextual features. The higher precision and robustness were also achieved in segmenting the occluded targets. A grasping score function was further developed for the complex stacking of the prickly ash branches. Three key factors were considered, including the branch-to-camera distance, mask completeness, and the entanglement risk between neighboring branches. A Bayesian optimization approach was also applied to determine the optimal weight coefficients of these factors, which were 0.797, 0.183, and 0.020, respectively. These coefficients were integrated with the depth information. The grasping score was computed to infer the optimal grasping sequence and then efficiently prioritize among stacked branches. Experimental results showed that the improved model significantly enhanced the performance of the branch recognition and grasping sequence under various stacking conditions. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) reached 86.68% and 91.04%, respectively. The precision, recall, and F1-score were 95.70%, 91.04%, and 92.82%, respectively, indicating an increase of 9.74, 9.75, and 4.99 percentage points, respectively, compared with the original model. Furthermore, the superior performance was achieved in the segmentation accuracy, boundary recognition, and robustness against occlusion, compared with the mainstream instance segmentation models, such as the Mask R-CNN, YOLACT, and YOLOv5. Grasping experiments were conducted on the practical harvesting operations in order to verify the effectiveness of the improved model. An AUBO-i10 robotic arm was equipped with a two-finger gripper and an Intel RealSense D435i depth camera in an eye-in-hand configuration. The robotic system successfully performed the detection, recognition, reasoning, and grasping of the prickly ash branches. The grasping success rate reached 75.86%, and the sequence reasoning accuracy was 86.21%. The feasibility and stability of the approach were obtained in the complex stacking scenarios. The reasoning strategy can be effectively applied to the grasping sequence inference for the intelligent prickly ash harvesters. The finding can also provide important references to optimize the automatic harvesting for green Sichuan pepper.

Feeding difficulty and easy clogging have confined the picking efficiency of green Sichuan pepper, leading to the “pile picking”. In this study, a rotating shear picking device was designed for green Sichuan pepper. Firstly, a picking scheme was determined, according to the physical and mechanical properties of prickly ash branches. The specific procedures included the rotating drive, guide feeding, and shearing picking of prickly ash branches. The reciprocating cutters were selected to realize the shear picking. The double-action reciprocating cutters were also designed for their transmission. The structure and motion parameters were determined to meet the requirements of rotary shear picking. Secondly, the simulation model was established to shear the prickly ash branch using ANSYS/LS-DYNA. The orthogonal tests were carried out with the cutting angle of the cutter tooth, the edge angle of the cutter tooth, and the thickness of the cutter tooth as the control factors, while the peak cutting force as the evaluation index. The results showed that the optimal tooth parameters were the cutting angle of 20 °, edge angle of 50°, and tooth thickness of 2.5 mm, with the peak cutting force 3.739 N, which was fully met the requirements of shear picking for the green Sichuan pepper. Finally, the prickly ash branches with fresh green Sichuan pepper were piled in the Sansheng Town, Beibei District, Chongqing, China, in June, 2023. A single-factor experiment was carried out with the branch feeding angle, branch feeding speed, and branch rotational speed as the control factors, while the picking efficiency, net collection rate, and fruit injury rate as the evaluation indices. The branch feeding angle, branch feeding speed, and branch rotation speed were determined in the range of 40°-60°, 20-40 mm/s, and 20-40 r/min, respectively. A three-factor quadratic regression orthogonal combination test was analyzed using Box-Behnken design on the Design-Expert 12. The optimal operating parameters were achieved in the branch feeding angle of 55.035°, the branch feeding speed of 32.04 mm/s, and the branch rotation speed of 29.635 r/min, in the rotating shear picking device for green Sichuan pepper. The performance was predicted as the picking efficiency of 10.78 kg/h, the net collection rate of 95.66%, and the fruit injury rate of 12.06%. The optimized parameters were rounded as the branch feeding angle of 55°, the branch feeding speed of 33.21 mm/s, and the branch rotational speed of 30r/min for the prototype test. Experiment validated that the average picking efficiency of a single person feeding a branch for the green Sichuan pepper was 10.95 kg/h using optimal operating parameters, with an average net collection rate of 95.57%, and an average fruit injury rate of 12.87%. The finding can provide the technical references to develop the picking machines for green Sichuan pepper.

In order to reduce the energy consumption of air source heat pump drying, a prediction method of energy consumption was proposed to optimize the drying process by means of multivariate linear regression model (MLRM) and back propagation neural network (BPNN) model. On the basis of analyzing characteristics of the energy consumption and factors that affect it, the drying process was proposed to split into sections of equal time to reduce the difficulty of data acquisition. Eight characteristic parameters were set as input, and two parameters were set as output. Input parameters were set temperature in drying room, set humidity in drying room, initial temperature in drying room, initial humidity in drying room, mean ambient temperature, mean ambient humidity, weight of material, and initial moisture content of material. Output parameters were energy consumption and end moisture content of material. Before build predictive models, the variation of the drying power of the air source heat pump was analysed. From the air source heat pump control principle and the drying power curve, the drying process was mainly divided into a heating stage and a insulation stage. The insulation stage consisted of a number of circulating insulation units. As the weight of pea increased, the duration of the heating stage increased, the duration of the insulation stage shortened. Meanwhile, the time of the insulation unit increased and the total drying time increased. Orthogonal experiment was designed using unit energy consumption (energy consumption required to reduce the moisture content of 1 kg of material by 1%) and total energy consumption as the evaluation indexes. According to the results of orthogonal experiment, among the three influencing factors of weight of pea, set temperature in drying room, and set humidity in drying room, the weight of pea had the most significant influence on unit energy consumption and total energy consumption, and the set humidity in drying room had slight significant effect on the total energy consumption. Based on characterization of energy consumption and moisture content of air source heat pump drying, the energy consumption was predicted using MLRM model and BPNN model. The corrective coefficients of determination of the MLRM model and BPNN model for energy consumption and end moisture content were 0.739 and 0.931, respectively. BPNN model with activation function of Sigmoid had the highest coefficient of determination of energy consumption, and the coefficient was 0.828. BPNN model with activation function of Identity had the highest coefficient of determination of end moisture content, and the coefficient was 0.942. The fitting effect was good and met the actual needs of production. Taking rehydrated peas as the drying object, a complete variable temperature and humidity drying process with a mass of 65 kg and a duration of 4 h was designed for verification test. The verification test results showed that the tested total energy consumption of the experiment was 15.066 kW·h, and the total energy consumption values predicted by MLRM model and BPNN model were 14.476 kW·h and 15.183 kW·h, with prediction accuracy of 96.08% and 99.23%, respectively. The tested end moisture content was 8.541%, and predicted moisture content by MLRM model and BPNN model were 9.560% and 8.889%, with prediction accuracies of 88.07% and 95.93%, respectively. This study analyzed the energy consumption characteristics of air source heat pump drying, and proposed an effective means for the prediction of energy consumption for air source heat pump drying, and the prediction was experimentally verified with high reliability of accuracy, which was of great practical significance for the drying process optimization and energy consumption reduction.

Navel orange is a widely cultivated variety of citrus in southern China. The annual output of citrus has exceeded 60 million tons, indicating the largest producer of citrus with planting area and output ranking first in the world in recent years. However, the damage to navel oranges has been caused by external compression during harvesting and transportation. In this study, an experimental test was conducted on the compression deformation of navel oranges. A systematic analysis was implemented to explore the influence of different compression scales on the damage degree of navel oranges. Some parameters were identified for the damage size of navel oranges. Firstly, the mechanical properties of navel orange were tested by an electronic universal testing machine. The key mechanical parameters of navel orange peel and pulp were measured, such as Young's modulus, yield strength, and Poisson's ratio. The transverse, longitudinal, and oblique compressive strength tests of navel orange were carried out on the load-displacement curves to calculate the ultimate load. The test results showed that the mean compressive limit load of navel orange in the transverse direction was lower than that in the longitudinal and oblique directions under the same deformation. Meanwhile, the navel orange was in the elastic deformation stage, when the compression displacement ranged from 0 to 7.5 mm. Once exceeding this range, the navel orange was in the plastic deformation stage. The damage levels of navel orange were determined under different compression displacements. The compression recovery coefficients of navel orange were obtained to measure the size change of navel orange before and after transverse compression. The microstructure change of the navel orange peel was observed by the paraffin section. At the same time, the navel orange samples after the compression test were stored at room temperature and dark environment. The mass-loss rate was measured regularly to evaluate the damage degree of navel orange under different compression conditions from macro and micro perspectives. The results showed that the fruit compression recovery coefficients fluctuated greatly between 10 and 12.5 mm when the compression load was applied to the navel orange. When the compression level was not more than 10 mm, the fruit compression recovery coefficient was close to 0.75. The peel oil cells were intact, indicating almost no damage to the navel orange fruit. Once the compression level reached 12.5 mm, the compression recovery coefficient of the navel orange was significantly reduced, indicating the outstandingly broken peel oil cell. Finally, the three-dimensional solid model of navel orange peel and pulp was constructed by 3D scanner and reverse engineering. The stress distribution of the whole navel orange peel and pulp was also simulated by ANSYS/LS-DYNA. The simulation results showed that the stress of the flesh tissue at a 12.5 mm compression level was very close to the ultimate yield stress. The external load first caused irreversible plastic deformation of the flesh tissue, leading to mechanical damage to the navel orange. The simulation results were consistent with the compression test, which verified the measured mechanical parameters of the navel orange. Therefore, the extrusion deformation range of navel orange should be controlled within 10 mm and the external load should not exceed 63.24 N in the process of low-loss harvesting and transportation. The findings can also provide a strong reference for the loss reduction and postharvest storage of navel oranges during harvesting and transportation.