ReviewFrontiers in plant science2020
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.
Review in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
81 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking.Frontiers in plant science · 2024Pooled it
- Boosting RGB-D Pear Detection via Depth-Constraint Enhanced Gaussian Prior.Plants (Basel, Switzerland) · 2026Article
- End-Effector Technologies for Fruit Harvesting Robots: A Review of Structures, Actuation, and Field Deployability.Sensors (Basel, Switzerland) · 2026Review
- Optimising Camera-ChArUco Geometry for Motion Compensation in Standing Equine CT: A CT-Motivated Benchtop Study.Sensors (Basel, Switzerland) · 2026Article
- A lightweight YOLO-based model for accurate detection of red pepper clusters in robotic harvesting.Scientific reports · 2026Article
- Intelligent high-throughput recognition model for bitter gourd fruit morphology and tubercle characteristics.Scientific reports · 2026Article
- YOLOv8-PnP fusion architecture for non-contact robotic pollination: a 6D pose estimation approach for autonomous greenhouse operations.Frontiers in plant science · 2026Article
- An improved YOLOv11n-based method for high-precision detection of ginkgo fruits in complex natural environments.Frontiers in plant science · 2026Article
- Cotton field population phenotyping analysis based on 3D Gaussian reconstruction and dynamic spatial constraints.Frontiers in plant science · 2026Article
- A real-time ripeness detection model for tomatoes in complex greenhouse environments.Frontiers in plant science · 2026Article
- A lightweight LBM detection transformer with multi-scale feature fusion for citrus-picking robots.Frontiers in plant science · 2026Article
- Real-time peach detection method in complex environments based on improved YOLOv8 and multi-attention fusion.Frontiers in plant science · 2026Article
- Detection method for munage grape clusters and abnormal berries under color-similar backgrounds.Frontiers in plant science · 2026Article
- Real-Time Object Detection for Edge Computing-Based Agricultural Automation: A Case Study Comparing the YOLOX and YOLOv12 Architectures and Their Performance in Potato Harvesting Systems.Sensors (Basel, Switzerland) · 2025Article
- Smart agriculture in Asia.Plant communications · 2025Review
- ELD-YOLO: A Lightweight Framework for Detecting Occluded Mandarin Fruits in Plant Research.Plants (Basel, Switzerland) · 2025Article
- Article
- Cherry-Net: real-time segmentation algorithm of cherry maturity based on improved PIDNet.Frontiers in plant science · 2025Article
- Tomato ripeness detection and fruit segmentation based on instance segmentation.Frontiers in plant science · 2025Article
- Design and experimental investigation of the grasping system of an agricultural soft manipulator based on FMDS-YOLOv8.Frontiers in plant science · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.
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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.