The Vision-Based Target Recognition, Localization, and Control for Harvesting Robots: A Review

Liu, Jingfan; Liu, Zhaobing

doi:10.1007/s12541-023-00911-7

Cited by 11 publications

(2 citation statements)

References 174 publications

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“…Machine vision-based methods for the segmentation and localization of A. bisporus have gained widespread application in agricultural harvesting robot platforms, owing to their advantages such as automation, real-time capability, noncontact nature, and repeatability [4,5]. These methods are extensively helpful in agricultural harvesting robot platforms [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

Shi,

Mo,

Ren

et al. 2024

Agriculture

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The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus. It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus.

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Section: Introductionmentioning

confidence: 99%

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

Shi,

Mo,

Ren

et al. 2024

Agriculture

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“…Now, they have become the primary tools for detecting agricultural products. These technologies have found extensive applications in remote intelligent monitoring [6,7], agricultural harvesting robots [8][9][10][11], ripeness detection [12], variety selection [13,14], yield prediction [15], and other related fields. As the field of object recognition technology continues to mature, experts from both domestic and international communities have increasingly integrated computer vision technology into the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

Zhang,

Huang,

Zhang

et al. 2023

Agriculture

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In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP, while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations.

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Bioconvergence: Bridging Science and Nature to Shape Tomorrow

Siragusa,

Matz

2024

Bioconvergence Revolution

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The Vision-Based Target Recognition, Localization, and Control for Harvesting Robots: A Review

Cited by 11 publications

References 174 publications

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

Bioconvergence: Bridging Science and Nature to Shape Tomorrow

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