Recognition of sweet peppers and planning the robotic picking sequence in high-density orchards

Ning, Zhengtong; Luo, Liang; Ding, XinMing; Dong, Zhiqiang; Yang, Baofeng; Cai, Jinghui; Chen, Weilin; Lu, Qinghua

doi:10.1016/j.compag.2022.106878

Cited by 32 publications

(16 citation statements)

References 18 publications

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“…Static analysis can be representative because of the low speed of the fingers when they work [46,47]. Since the three clamping fingers and the crank slider mechanism are symmetrically arranged along the center of the ball screw, one side is taken for analysis, and the clamping fingers of the end-effector are composed of a four-link drive mechanism, which can be known according to the vector analysis method in the mechanical principles.…”

Section: Clamping Mechanism Parameter Settingmentioning

confidence: 99%

End-Effectors Developed for Citrus and Other Spherical Crops

Wang

Jiang

2022

Applied Sciences

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Citrus harvesting is time-intensive and labor-intensive, relying mainly on manual harvesting. The automatic harvesting of fruit and vegetable crops can not only reduce the physical labor of fruit farmers in the harsh field environment but also greatly improve the harvesting efficiency. Based on the principle of manual citrus picking, an end-effector with three-finger grasping is designed in this study. First, the structure of the end-effector was designed to achieve the function of stable grasping and effective cutting of citrus fruits, and then the working process and key parameters of the end-effector were explained in detail. Finally, a picking test was conducted without considering robot vision. The test results show that the end-effector has a picking success rate of 95.23% for citrus with a diameter of 30–100 mm and an average picking time of 4.65 s for a single fruit. This end-effector can realize the picking function for citrus of different sizes and shapes and has the advantages of high adaptability, stable gripping and no damage to the fruit.

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Section: Clamping Mechanism Parameter Settingmentioning

confidence: 99%

End-Effectors Developed for Citrus and Other Spherical Crops

Wang

Jiang

2022

Applied Sciences

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“…Currently, pepper-picking machines are gradually developing towards automation. Ning et al conducted path planning for an automatic chili-picking device [3]. Liu used deep cameras and deep learning algorithms for recognition and localization during the pepper-picking process [4].…”

Section: Introductionmentioning

confidence: 99%

Design and Experimental Study of a Biomimetic Pod-Pepper-Picking Drum Based on Multi-Finger Collaboration

Du,

Fang,

Han

et al. 2024

Agriculture

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In order to reduce ground drop loss during mechanical pepper picking and improve the net recovery rate, a drum snap finger picking device was designed. The picking device is mainly composed of a picking drum and auxiliary picking components; the picking finger arrangement was designed biomimetically and its structure and operating parameters were optimized by the DEM (discrete element method). According to the physical and mechanical characteristics of the pepper and the simplified three-dimensional model of the picking device, a virtual simulation model of the pepper-picking device was established using the EDEM software. Through simulation analysis and using the orthogonal test method, the main factors which affect the ground drop loss rate of pepper and their optimal parameter combination values were determined. The simulation results were verified by a pepper-picking field experiment. Orthogonal tests show that, when the picking drum speed (V′) is 210 rpm, the pepper-feeding speed (V″) is 1100 mm·s−1, the bending angle of each picking spring tooth (C) is 162°, and each group of circumferential fingers has rows, the picking device has a good picking effect. At this time, the ground drop loss rates in both the simulation and field test were 7.50% and 7.85%, respectively, and the drop error was only 4.46%, which was within the allowable range. The design form and parameter optimization simulation method in this paper provide an important reference for the design and optimization of pepper-harvesting machinery.

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“…In recent decades, systems based on computer vision have been largely applied to detect and classify fruits (Bazame et al, 2021;Ning et al, 2022;Thendral and David, 2022;Wang et al, 2019;Wu et al, 2020a). Few studies have reported on the classification of coffee fruits before the harvest, which can aid the decisionmaking of coffee farmers (Avendano et al, 2017;Ramos et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Detection of coffee fruits on tree branches using computer vision

Bazame

Molin

Althoff

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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Recognition of sweet peppers and planning the robotic picking sequence in high-density orchards

Cited by 32 publications

References 18 publications

End-Effectors Developed for Citrus and Other Spherical Crops

End-Effectors Developed for Citrus and Other Spherical Crops

Design and Experimental Study of a Biomimetic Pod-Pepper-Picking Drum Based on Multi-Finger Collaboration

Detection of coffee fruits on tree branches using computer vision

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