ArticleSensors (Basel, Switzerland)2019
Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 156 citations in OpenAlex.
- Tapping line detection and rubber tapping pose estimation for natural rubber trees based on improved YOLOv8 and RGB-D information fusion.Scientific reports · 2024Article
- A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8.Frontiers in plant science · 2024Article
- Recognition and Counting of Apples in a Dynamic State Using a 3D Camera and Deep Learning Algorithms for Robotic Harvesting Systems.Sensors (Basel, Switzerland) · 2023Article
- A Survey of Robotic Harvesting Systems and Enabling Technologies.Journal of intelligent & robotic systems · 2023Review
- Real-time guava tree-part segmentation using fully convolutional network with channel and spatial attention.Frontiers in plant science · 2022Article
- 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis.Zoological research · 2021Article
- Soft Grippers for Automatic Crop Harvesting: A Review.Sensors (Basel, Switzerland) · 2021Review
- Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques-A Review.Frontiers in plant science · 2021Review
- Fruit Detection and Pose Estimation for Grape Cluster-Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks.Frontiers in robotics and AI · 2021Article
- Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting.Sensors (Basel, Switzerland) · 2020Article
- Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner.Sensors (Basel, Switzerland) · 2020Article
- Cutting Pose Prediction from Point Clouds.Sensors (Basel, Switzerland) · 2020Article
- Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis.Sensors (Basel, Switzerland) · 2020Article
- Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.Frontiers in plant science · 2020Review
- Fruit Detection and Segmentation for AppleHarvesting Using Visual Sensor in Orchards.Sensors (Basel, Switzerland) · 2019Article
- RGB-D-Based Pose Estimation of Workpieces with Semantic Segmentation and Point Cloud Registration.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red⁻green⁻blue⁻depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot.
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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.