ArticleSensors (Basel, Switzerland)2020
Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 176 citations in OpenAlex.
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- Design and experiment of miss-seeding detection and preparatory seed scraper-belt compensation mechanism based on improved YOLOv5s for potato seed-metering devices.Frontiers in plant science · 2025Article
- Gripping Success Metric for Robotic Fruit Harvesting.Sensors (Basel, Switzerland) · 2024Article
- YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.Scientific reports · 2024Article
- FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting.Sensors (Basel, Switzerland) · 2024Article
- 3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.Sensors (Basel, Switzerland) · 2024Article
- A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8.Frontiers in plant science · 2024Article
- A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation: leveraging dilated residual and multi-screening path aggregation.Frontiers in plant science · 2024Article
- TCSRNet: a lightweight tobacco leaf curing stage recognition network model.Frontiers in plant science · 2024Article
- Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization.Sensors (Basel, Switzerland) · 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
- Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.Sensors (Basel, Switzerland) · 2022Article
- Tableware Tidying-Up Robot System for Self-Service Restaurant-Detection and Manipulation of Leftover Food and Tableware.Sensors (Basel, Switzerland) · 2022Article
- Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.Sensors (Basel, Switzerland) · 2022Article
- Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods.Plant methods · 2022Article
- Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.Frontiers in plant science · 2022Article
- Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning-Based Vision System Combining Detection and Tracking for Fast On-Line Citrus Sorting.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Robotic harvesting shows a promising aspect in future development of agricultural industry. However, there are many challenges which are still presented in the development of a fully functional robotic harvesting system. Vision is one of the most important keys among these challenges. Traditional vision methods always suffer from defects in accuracy, robustness, and efficiency in real implementation environments. In this work, a fully deep learning-based vision method for autonomous apple harvesting is developed and evaluated. The developed method includes a light-weight one-stage detection and segmentation network for fruit recognition and a PointNet to process the point clouds and estimate a proper approach pose for each fruit before grasping. Fruit recognition network takes raw inputs from RGB-D camera and performs fruit detection and instance segmentation on RGB images. The PointNet grasping network combines depth information and results from the fruit recognition as input and outputs the approach pose of each fruit for robotic arm execution. The developed vision method is evaluated on RGB-D image data which are collected from both laboratory and orchard environments. Robotic harvesting experiments in both indoor and outdoor conditions are also included to validate the performance of the developed harvesting system. Experimental results show that the developed vision method can perform highly efficient and accurate to guide robotic harvesting. Overall, the developed robotic harvesting system achieves 0.8 on harvesting success rate and cycle time is 6.5 seconds.
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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.