ArticleSensors (Basel, Switzerland)2022
Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Identification of a 301 bp promoter core region of the SrUGT91D2 gene from Stevia rebaudiana that contributes to hormone and abiotic stress inducibility.BMC plant biology · 2024Article
- A Variable Photo-Model Method for Object Pose and Size Estimation with Stereo Vision in a Complex Home Scene.Sensors (Basel, Switzerland) · 2023Article
- Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning.Sensors (Basel, Switzerland) · 2023Review
- Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
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Registered trials
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