Design of an efficient combined multipoint picking scheme for tea buds

Xu, Lijia; Chen, Xinyuan; Chen, Yanjun; Kang, Zhiliang; Huang, Peng; Zou, Zhiyong; He, Yong; Yang, Ning; Peng, Yingqi; Wu, Zhijun; Bi, Liu; Wang, Yuchao; Zhao, Yongpeng

doi:10.3389/fpls.2022.1042035

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…This implementation will enable the precise picking of tender tea leaves. Previous studies have developed computer vision systems using robotics and deep learning techniques for intelligent tea bud harvesting [ 53 ], a spatial positioning system for fruits [ 54 ], and the dimensions of tea bud harvesters [ 55 ], which provide theoretical support and insights on intelligent tea bud harvesting. However, there are still many aspects to consider in realizing a fully automated harvesting system.…”

Section: Discussionmentioning

confidence: 99%

Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision

Xie

Sun

2023

Sensors

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Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment.

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Section: Discussionmentioning

confidence: 99%

Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision

Xie

Sun

2023

Sensors

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“…To improve the operational efficiency and success rate of picking robots, researchers have gradually shifted their focus to pickingpath planning algorithms and picking decision systems based on fruit detection (Lin et al, 2021;. For example, Xu et al (2022) have proposed an efficient combined multipoint picking scheme for tea buds through a greedy algorithm and ant colony algorithm, which improved picking efficiency and overall picking success rate. Ning et al (2022) proposed a method for recognition and planning robotic picking sequences for sweet peppers based on an improved YOLOV4 model and a principle of anticollision picking within picking clusters.…”

Section: Introductionmentioning

confidence: 99%

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

et al. 2023

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Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.

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“…In recent years, scholars have explored systems used to harvest premium tea shoots. Xu L. et al. (2022) designed a picking box with clips cut on both sides.…”

Section: Introductionmentioning

confidence: 99%

Continuous identification of the tea shoot tip and accurate positioning of picking points for a harvesting from standard plantations

Luo,

Zhang,

Cao

et al. 2023

Front. Plant Sci.

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To address the current problems of large positioning error, low picking efficiency, and high cost of tea shoot picking, a continuous and precise harvesting scheme for tea shoots based on a two-dimensional (2D) perspective is designed in this study. A high-speed harvesting method for tea shoots in a standardized tea plantation assembly line type was proposed. First, a 2D view recognition model of tea shoot tips in a multi-disturbance environment was constructed, and accurate picking point coordinates were determined by combining a skeleton algorithm and curve growth. To avoid the losses of recognition accuracy caused by the mistaken clamping of blades and vibrations during harvester operations, accurate control of the harvester was realized by combining path planning and the S-curve speed control function. The recognition accuracy for the verification set of the recognition model was 99.9%, and the mean average precision (0.5:0.95) value was 0.97. The test results show that the error between the actual picking point position and the position determined by the model was within ± 3 mm, and the picking success rate was 83.6%. Therefore, we can realize fast and accurate picking of tea shoots and lay the foundation for continuous tea picking in the future by simplifying the identification and picking process.

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Design of an efficient combined multipoint picking scheme for tea buds

Cited by 6 publications

References 33 publications

Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision

Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

Continuous identification of the tea shoot tip and accurate positioning of picking points for a harvesting from standard plantations

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