Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the interval values were measured through angle of repose tests of fallen jujube fruit (FJF), and the simulation angle of repose tests for FJF were established with EDEM software (DEM Solutions Ltd. Edinburgh, Scotland, UK). Then, the Plackett-Burman design, steepest ascent search experiment, and center composite design experimental methods were utilized to obtain the specific values of the simulation parameters from the interval values. The results showed that significant influencing factors in the simulation angle of repose include the Poisson’s ratio, the static friction coefficient between FJF, and the static friction coefficient between FJF and the steel plate, for which the optimal values were 0.248, 0.480, and 0.309, respectively. The angle of repose tests’ results showed that the error was 0.53% between the simulation angle of repose (29.69°) and the angle of repose (29.85°). In addition, the flow rate test results showed that the average error was 5.84% between the physical and simulation tests. This indicated that the calibrated parameters were accurate and reliable, and that the simulation model can accurately represent the physical tests. Consequently, this study provides an EDEM model of FJF that was essential in designing machinery and equipment through the EDEM simulation method.

Low working efficiency is an important reason for the limited application of the traditional aspirated-air type jujube fruit pickup machine. In this study, a provoke-suction type harvester for ground jujube fruit (PSH) was designed, based on the principle of negative pressure suction after shoveling the jujube fruit mechanically. The main working parameters were analyzed and the structures of the key devices were designed. Then, a three-factor and three-level Box–Behnken method was used to evaluate the performance of the PSH. The results showed that the pickup rate, impurity rate, and working efficiency were 99.36%, 5.63%, and 1672.3 kg∙h⁻¹, respectively; as the forward speed, provoke teeth buried depth, and airflow velocity were 0.21 kg∙h⁻¹, 74 mm, and 26.4 kg∙h⁻¹, respectively. Furthermore, the verification results showed that the pickup rate, impurity rate, and working efficiency were 98.05%, 5.97%, and 1591.2 kg∙h⁻¹, respectively, moreover, the relative errors were 1.32%, 6.04%, and 4.85%, respectively, indicating that the parameter optimization model can accurately predict the test results. The working efficiency of the PSH was significantly improved compared with the traditional aspirated-air type jujube fruit pickup machine. This research can provide a reference for the development of the jujube fruit pickup machine.

The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CA_H attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81.2% to 90.1%, and the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to that of the YOLOv8s base model, with precision increasing by 2.4% to 92.3%, recall increasing by 1.4%, mAP@0.5 increasing by 1.8%, and FPS being 17.1% faster.

The poor adaptability of existing harvesting machinery and low work efficiency is observed due to the low-level mechanized harvesting of Xinjiang jujube orchards. A mechanical floor-standing jujube picker was designed on the basis of the characteristics of the high fruit drop of the Xinjiang jujube harvest period. The machine can pick up the jujubes, which have been manually collected into strips on the ground. The structure and working parameters of the pickup device are determined in accordance with the pickup principle. The conditions for satisfying the picking operation were analyzed, and the range of motion speed ratio parameters to meet the pickup operation was obtained. Furthermore, the contact process of jujubes on the conveying and separating device was analyzed and studied; the factors affecting the collision damage and conveying the balance of jujubes were analyzed; and the basic structural parameters of the conveying and separating devices were determined. A mechanical floor-standing jujube picker was also fabricated for trial. A three-factor and three-level Box– Behnken test was performed by taking the forward speed of the machine, the speed of the strip brush roller shaft, and the hole pitch height of the profiling rod as factors, and the pickup, breakage, and impurity rates of jujubes as indicators. Results show that when the forward speed of the machine was 0.3 m·s⁻¹, the speed of the strip brush roller shaft was 53 rpm and the hole pitch height of the profiling rod was 60 mm. The jujube pickup rate of the machine was 92.11%, the breakage rate was 2.07%, and the impurity rate was 4.15%. The relative error with the prediction model was less than 8%. Thus, the model is reliable and meets the operational requirements. This study can provide a reference for the mechanical floor-standing jujube picker.

In view of the problem whereby the floor-standing jujube pickup device designed by the research group has a large resistance when the comb teeth (CT) enter the soil, the strip brush can easily bend, and the operation effect is poor. In this paper, the structural parameters of the CT and bar brush in the mechanical floor date-picking device are determined by theoretical analysis. EDEM software was used to establish the discrete element simulation model of the floor-standing jujube soil–jujube pickup device. According to the simulation results, it is determined that the angle of the CT entering the soil (ACT) can change freely within 30–33° and meet the operation requirements. Through the single-factor test on the pickup rate of jujube and the soil hilling quantity, it is determined that the value range of the CT working speed (CWS) is 0.2–0.4 m/s, and the value range of the CT entering the soil (DCS) is 5–45 mm. Simulation and field verification tests were carried out on the determined operation parameter range. It was found that when the CWS was 0.2 m/s and the DCS was 5 mm, the pickup rates in the simulation test and the field verification test were 84.17% and 91.23%, respectively, and the relative error was the largest but not more than 8%. The result shows that the operation parameters and range determined by the discrete element method were reliable. This study is expected to provide the working parameter basis for the subsequent design of a floor-standing jujube pickup device.

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.