ReviewSensors (Basel, Switzerland)2026
End-Effector Technologies for Fruit Harvesting Robots: A Review of Structures, Actuation, and Field Deployability.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This review summarizes the research on the end effectors of agricultural harvesting robots (2010-2025) and extracts two core design principles. First of all, the selection of end effectors must follow the biological characteristics of fruits: rigid grippers are suitable for hard skinned and regular fruits; soft grippers can reduce the damage of fragile crops to a certain extent; suction cups are suitable for smooth, barrier free surfaces; the envelope type is suitable for soft and lossless picking scenes; the combined suction and grip design is more suitable for unstructured environments. Secondly, the separation mode should match the characteristics of the stem: motion separation (torsion/pull) is suitable for weak stems, while cutting is mainly used for hard stems. Unlike previous literature, this review provides a field deployability checklist (including dust/water proofing, cleanliness, maintenance, aging prevention, and aspiration prevention) to narrow the results of the laboratory and the real field environment. The three future directions of multimodal perception, variable stiffness driving and reinforcement learning are logically related to the analysis in this paper: multimodal perception optimizes the perception limit, variable stiffness solves the rigid-flexible trade-off, and reinforcement learning provides adaptive strategies for different crops. This framework can match the end effector design with the crop-specific field conditions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.