An end-to-end lightweight model for grape and picking point simultaneous detection

Zhao, Ruzhun; Zhu, Yuchang; Li, Yuanhong

doi:10.1016/j.biosystemseng.2022.08.013

Cited by 29 publications

(2 citation statements)

References 34 publications

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“…Zhao. et al [28] proposed a lightweight end-to-end model of YOLO-GP to simultaneously detect grape clusters and predict the picking point based on the grape bounding boxes. Although the interference of the background was reduced, the impact of different end effectors on the estimation and calculation of the picking point was not considered, which may easily lead to the inability of the end effector to accurately clamp the grape stem.…”

Section: Introductionmentioning

confidence: 99%

Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting

Liu

Wang

et al. 2023

Agronomy

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For high-speed robotic cut-and-catch harvesting, efficient trellis grape recognition and picking point positioning are crucial factors. In this study, a new method for the rapid positioning of picking points based on synchronous inference for multi-grapes was proposed. Firstly, a three-dimensional region of interest for a finite number of grapes was constructed according to the “eye to hand” configuration. Then, a feature-enhanced recognition deep learning model called YOLO v4-SE combined with multi-channel inputs of RGB and depth images was put forward to identify occluded or overlapping grapes and synchronously infer picking points upwards of the prediction boxes of the multi-grapes imaged completely in the three-dimensional region of interest (ROI). Finally, the accuracy of each dimension of the picking points was corrected, and the global continuous picking sequence was planned in the three-dimensional ROI. The recognition experiment in the field showed that YOLO v4-SE has good detection performance in various samples with different interference. The positioning experiment, using a different number of grape bunches from the field, demonstrated that the average recognition success rate is 97% and the average positioning success rate is 93.5%; the average recognition time is 0.0864 s; and the average positioning time is 0.0842 s. The average positioning errors of the x, y, and z directions are 2.598, 2.012, and 1.378 mm, respectively. The average positioning error of the Euclidean distance between the true picking point and the predicted picking point is 7.69 mm. In field synchronous harvesting experiments with different fruiting densities, the average recognition success rate is 97%; the average positioning success rate is 93.606%; and the average picking success rate is 92.78%. The average picking speed is 6.18 s×bunch−1, which meets the harvesting requirements for high-speed cut-and-catch harvesting robots. This method is promising for overcoming time-consuming harvesting caused by the problematic positioning of the grape stem.

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Section: Introductionmentioning

confidence: 99%

Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting

Liu

Wang

et al. 2023

Agronomy

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“…The mAP of target detection by the improved SSD algorithm was 97.32%, and the speed of detection is 41.15 FPS. Ruzhun Zhao et al [12] built a lightweight end-to-end model, YOLO-GP, for the accurate detection of grape clusters and their picking points, and the mAP reached 93.27% while reducing certain parameter weights. Zhaoyi Chen et al [13] designed a plant disease detection model, a new involute bottleneck module is used to capture remote information in space while reducing network parameters and computation.…”

Section: Introductionmentioning

confidence: 99%

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

Xue

2023

Preprint

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The efficient detection of strawberries has great significance in the realization of strawberry production estimation and automatic picking in the greenhouse. Factors such as the complex growing environment in the field and fruit aggregation shading, especially for immature strawberries at the turning stage and mature strawberries with high similarity in shape, size, and even color, resulted in low accuracy of detection. This research aims at establishing an improved lightweight model based on YOLOv5 which is for strawberry ripeness detection in natural environments, in response to the problems of some current detection models whose structures are complex and difficult to deploy on low-cost devices. The method first adds a smaller target detection layer to the original network, then removes part of the deep structure of the network to reduce the complexity of the model, by adjusting the depth-multiple and width-multiple partially to achieve a lighter model while ensuring accuracy. After that, the performance of the model was further improved by introducing the BiFPN structure and SimAM attention module. The experimental results show that the improved model provides an effective method for detecting strawberries in natural environments. Compared with the YOLOv5s model, the improved model has a 60.35% reduction in parameters and a 55.47% reduction in the model size, and the mAP, mAP0.5:0.95, and F1 are improved to 91.86%, 79.04%, and 90.9%, respectively. The model proposed in this research proves an excellent generalization performance in three strawberry test sets and provides a reference for subsequent deployment in small/low-cost picking robots.

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Recent Advances in Intelligent Harvesting Robots

Yun,

An,

et al. 2024

Smart Agriculture

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An end-to-end lightweight model for grape and picking point simultaneous detection

Cited by 29 publications

References 34 publications

Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting

Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

Recent Advances in Intelligent Harvesting Robots

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