ArticleScientific reports2024
Intelligent robotics harvesting system process for fruits grasping prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Partially occluded weed classification using vision transformers and convolutional neural networks for precision agriculture.Scientific reports · 2026Article
- Image-Based Segregation of High-Quality Dragon Fruits Among Ripe Fruits.Sensors (Basel, Switzerland) · 2026Article
- Optimization of a bionic end-effector for automated cherry tomato harvesting using Response Surface Methodology and Box-Behnken design.PloS one · 2026Article
- A framework for robotic manipulation tasks based on multiple zero shot models.Scientific reports · 2025Article
- A vision model for automated frozen tuna processing.Scientific reports · 2025Article
- Gripping Success Metric for Robotic Fruit Harvesting.Sensors (Basel, Switzerland) · 2024Article
- An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.
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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.