Jie Deng

The integration of agricultural science, artificial intelligence, and robotics has accelerated the development of robotic arms, significantly enhancing their potential applications across various fields. In light of the unique characteristics of agricultural environments, this paper provides a comprehensive review of variable-stiffness control methods for robotic arms. A robotic arm with rigid–flexible switching capabilities can effectively address collision safety concerns in complex agricultural environments, offering promising research prospects. This paper outlines recent advancements in variable-stiffness control techniques, including passive control methods based on materials and flexible devices, active control methods based on algorithms, and hybrid methods that combine control algorithms with flexible devices. Additionally, it discusses the technical challenges and future opportunities regarding deploying variable-stiffness robotic arms in agricultural environments. The aim of this paper is to provide theoretical and technical insights to support the efficient and versatile development of agricultural robots.

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.