For high-speed robotic cut-and-catch harvesting, efficient trellis grape recognition and picking point positioning are crucial factors. In this study, a new method for the rapid positioning of picking points based on synchronous inference for multi-grapes was proposed. Firstly, a three-dimensional region of interest for a finite number of grapes was constructed according to the “eye to hand” configuration. Then, a feature-enhanced recognition deep learning model called YOLO v4-SE combined with multi-channel inputs of RGB and depth images was put forward to identify occluded or overlapping grapes and synchronously infer picking points upwards of the prediction boxes of the multi-grapes imaged completely in the three-dimensional region of interest (ROI). Finally, the accuracy of each dimension of the picking points was corrected, and the global continuous picking sequence was planned in the three-dimensional ROI. The recognition experiment in the field showed that YOLO v4-SE has good detection performance in various samples with different interference. The positioning experiment, using a different number of grape bunches from the field, demonstrated that the average recognition success rate is 97% and the average positioning success rate is 93.5%; the average recognition time is 0.0864 s; and the average positioning time is 0.0842 s. The average positioning errors of the x, y, and z directions are 2.598, 2.012, and 1.378 mm, respectively. The average positioning error of the Euclidean distance between the true picking point and the predicted picking point is 7.69 mm. In field synchronous harvesting experiments with different fruiting densities, the average recognition success rate is 97%; the average positioning success rate is 93.606%; and the average picking success rate is 92.78%. The average picking speed is 6.18 $\mathrm{s}\times {\mathrm{bunch}}^{-1}$, which meets the harvesting requirements for high-speed cut-and-catch harvesting robots. This method is promising for overcoming time-consuming harvesting caused by the problematic positioning of the grape stem.

In harvesting operations, simulation verification of hand–eye coordination in a virtual canopy is critical for harvesting robot research. More realistic scenarios, vision-based driving motion, and cross-platform interaction information are needed to achieve such simulations, which are very challenging. Current simulations are more focused on path planning operations for consistency scenarios, which are far from satisfying the requirements. To this end, a new approach of visual servo multi-interaction simulation in real scenarios is proposed. In this study, a dual-arm grape harvesting robot in the laboratory is used as an example. To overcome these challenges, a multi-software federation is first proposed to establish their communication and cross-software sending of image information, coordinate information, and control commands. Then, the fruit recognition and positioning algorithm, forward and inverse kinematic model and simulation model are embedded in OpenCV and MATLAB, respectively, to drive the simulation run of the robot in V-REP, thus realizing the multi-interaction simulation of hand–eye coordination in virtual trellis vineyard. Finally, the simulation is verified, and the results show that the average running time of a string-picking simulation system is 6.5 s, and the success rate of accurate picking point grasping reached 83.3%. A complex closed loop of “scene-image recognition-grasping” is formed by data processing and transmission of various information. It can effectively realize the continuous hand–eye coordination multi-interaction simulation of the harvesting robot under the virtual environment.

The land used for fruit cultivation now exceeds 120 million hectares globally, with an annual yield of nearly 940 million tons. Fruit picking, the most labor-intensive task in agricultural production, is gradually shifting toward automation using intelligent robotic systems. As the component in direct contact with crops, specialized picking end-effectors perform well for certain fruits but lack adaptability to diverse fruit types and canopy structures. This limitation has constrained technological progress and slowed industrial deployment. The diversity of fruit shapes and the wide variation in damage thresholds—2–4 N for strawberries, 15–40 N for apples, and about 180 N for kiwifruit—further highlight the challenge of universal end-effector design. This review examines two major technical pathways: separation mechanisms and grasping strategies. Research has focused on how fruits are detached and how they can be securely held. Recent advances and limitations in both approaches are systematically analyzed. Most prototypes have achieved picking success rates exceeding 80%, with average cycle times reduced to 4–5 s per fruit. However, most designs remain at Technology Readiness Levels (TRLs) 3–5, with only a few reaching TRLs 6–7 in greenhouse trials. A dedicated section also discusses advanced technologies, including tactile sensing, smart materials, and artificial intelligence, which are driving the next generation of picking end-effectors. Finally, challenges and future trends for highly universal agricultural end-effectors are summarized. Humanoid picking hands represent an important direction for the development of universal picking end-effectors. The insights from this review are expected to accelerate the industrialization and large-scale adoption of robotic picking systems.

Mechanical stress induced by compression during preharvest and postharvest handling of fresh fruit is a major factor influencing the quality of fresh fruit. The degree of compression primarily governs the extent of quality deterioration. However, research on the damage mechanisms of mechanical compression in table grapes remains scarce. This investigation evaluated the impact of varying compression levels (0%, 20%, 40%, 60%, and 80%) on the postharvest quality attributes of table grapes. Changes in postharvest physical properties (overall appearance and color), structural properties (firmness, springiness, cohesiveness, and chewiness), physiological qualities (total soluble solids, titratable acidity, ascorbic acid, malonaldehyde content, and relative electrical conductivity), and cell microstructure of the berries was determined at 4 °C during 15 d of storage. Mechanical compression contributed to the deterioration of the quality of table grapes during storage, resulting in increased weight loss, decay rate, malonaldehyde content, and relative electrical conductivity; and decreased total soluble solids, titratable acidity, and ascorbic acid content. Furthermore, as compared to the control group, mechanical compression resulted in substantial yellowing and diminished textural qualities of grapes. In particular, compression treatment caused significant deformation of grape cell microstructure. In conclusion, mechanical compression stress significantly affects the physical and physiological properties of postharvest table grapes, as well as the internal cellular organization. As compression levels increase, the quality of table grapes progressively deteriorates, leading to a substantial reduction in storage life and commercial value. This study offers essential information for devising damage prevention strategies in preharvest and postharvest handling of table grapes.

The process of agricultural robot design is a complex system requiring the cooperation and integration of agricultural, machinery, automation, and information technology. These demands create great challenges for the innovative design of agricultural robots. Meanwhile, more than 95% of the latest inventions and creations in the world are recorded in the patent literature. In order to make effective use of the information and data resources of patents, shorten the design cycle, and provide knowledge for the designers, according to the operation’s objectives, an agricultural robot technology knowledge graph (TKG) was established for innovative designs. By analyzing the patent information, a patent IPC co-classification network (IPCNet) for adaptive design process recognition was put forward to meet the requirements of the different operation objectives and operation links. Through the extraction of the technology keywords and efficacy keywords, based on the word co-occurrence network (WCONet), a technology–efficacy map (TEM) was constructed. Through the integration of the adaptive design process and the TEM, the agricultural robot design TKG was constructed for determining technological recommendations for agricultural robot design. The case of the citrus picking robot design was realized to implement the design process. With the technology recommendation results, the moving system, body, and end-effector for the citrus picking robot were designed to verify the results of the recommendation.

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. However, there is still a lack of consistent empirical evidence regarding the combined emission reduction effect of these three factors in OECD countries. This study aims to empirically examine the combined impact of green technology innovation (GTI), renewable energy consumption (REC), and environmental taxes (ETAX) on carbon dioxide emissions. We expect that the former two will effectively reduce emissions, while the effect of environmental taxes depends on their design. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for the robustness test. To control potential endogenous issues, the difference generalized method of moments (GMM) is also employed for estimation. The causal relationship between variables is tested using the Dumitrescu–Herlin method. The results show that, as expected, GTI and REC have a significant negative impact on carbon dioxide reduction. However, ETAX is positively correlated with carbon emissions and does not have statistical significance, which deviates from the ideal policy effect and suggests that there may be efficiency bottlenecks in the current tax design. The causality test further reveals that there is a significant two-way causal relationship between CO₂ emissions and GTI, REC, ETAX, GDP, and fossil fuel consumption (FEC). Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low-carbon economy.