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.

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.