ArticleJournal of field robotics2020
A field-tested robotic harvesting system for iceberg lettuce.
Article in Journal of field robotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed.
- Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Journal of integrative plant biology · 2026Article
- A compliant, morphing gripper for handling diverse leafy vegetables in vertical farming systems.Scientific reports · 2026Article
- Fusion of Robotics, AI, and Thermal Imaging Technologies for Intelligent Precision Agriculture Systems.Sensors (Basel, Switzerland) · 2025Review
- A review of visual perception technology for intelligent fruit harvesting robots.Frontiers in plant science · 2025Review
- Implications of Developmental Levels and Packaging Materials on the Quality of Iceberg Lettuce for Marketing System: A Review.International journal of food science · 2025Review
- Effects of Harvesting Grabbing Type on Grabbing Force and Leaf Injury of Lettuce.Sensors (Basel, Switzerland) · 2023Article
- Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects.Sensors (Basel, Switzerland) · 2023Review
- Boosting precision crop protection towards agriculture 5.0Frontiers in plant science · 2023Review
- Soft robotics towards sustainable development goals and climate actions.Frontiers in robotics and AI · 2023Article
- Grasping and cutting points detection method for the harvesting of dome-type planted pumpkin using transformer network-based instance segmentation architecture.Frontiers in plant science · 2023Article
- Biologically inspired robotic perception-action for soft fruit harvesting in vertical growing environments.Precision agriculture · 2023Article
- Mobile robotics in smart farming: current trends and applications.Frontiers in artificial intelligence · 2023Review
- Structural Optimisation and Design of a Cable-Driven Hyper-Redundant Manipulator for Confined Semi-Structured Environments.Sensors (Basel, Switzerland) · 2022Article
- Article
- Soft Grippers for Automatic Crop Harvesting: A Review.Sensors (Basel, Switzerland) · 2021Review
- Analysis of the Pneumatic System Parameters of the Suction Cup Integrated with the Head for Harvesting Strawberry Fruit.Sensors (Basel, Switzerland) · 2020Article
- A field-tested robotic harvesting system for iceberg lettuce.Journal of field robotics · 2020Article
- Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.Frontiers in plant science · 2020Review
- The Plant Health Monitoring System of the EDEN ISS Space Greenhouse in Antarctica During the 2018 Experiment Phase.Frontiers in plant science · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state-of-the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.
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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.