Fast recognition method for citrus under complex environments based on improved YOLOv3

Xiao, Xu; Huang, Jingjing; Li, Ming; Xu, Yang; Zhang, Hongduo; Wen, Chaowu; Dai, Sihui

doi:10.1049/tje2.12097

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Some researchers have achieved high-speed CNN-based detection models, which are required for real-time performance. For example, a detection time of only 9.9 ms per frame was achieved when applied to citrus fruit (Xiao et al , 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

Peebles,

Lim,

Duke

et al. 2024

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Purpose Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting. Design/methodology/approach A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions. Findings The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements. Originality/value Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

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Section: Literature Reviewmentioning

confidence: 99%

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

Peebles,

Lim,

Duke

et al. 2024

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“…Several studies have proposed improved backbone feature extraction network schemes. Hunan Agricultural University [8] designed a new backbone feature extraction network based on YOLOv3 for fast detection of citrus fruits in natural environments. The accuracy rate of this method is 94.3%, with a detection time of 0.01 s. Qingdao Agricultural University [68] combined the fast detection capability of YOLOv3 with the high-precision classification ability of DenseNet201, enabling precise detection of tea shoots.…”

Section: Object Detection Algorithmmentioning

confidence: 99%

“…Common algorithms include image segmentation, object detection, and three-dimensional reconstruction. Image segmentation algorithms can segment target objects in complex scenes [7], object detection algorithms can timely detect target objects and other interferences in images [8], while three-dimensional reconstruction algorithms can convert the two-dimensional image information obtained by the camera into three-dimensional spatial information of the target [9].…”

Section: Introductionmentioning

confidence: 99%

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Hou,

Chen,

Jiang

et al. 2023

Agriculture

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Intelligent agriculture imposes higher requirements on the recognition and localization of fruit and vegetable picking robots. Due to its unique visual information and relatively low hardware cost, machine vision is widely applied in the recognition and localization of fruit and vegetable picking robots. This article provides an overview of the application of machine vision in the recognition and localization of fruit and vegetable picking robots. Firstly, the advantages, disadvantages, and the roles of different visual sensors and machine vision algorithms in the recognition and localization of fruit and vegetable picking robots are introduced, including monocular cameras, stereo cameras, structured light cameras, multispectral cameras, image segmentation algorithms, object detection algorithms, and 3D reconstruction algorithms. Then, the current status and challenges faced by machine vision in the recognition and localization of fruit and vegetable picking robots are summarized. These challenges include the stability of fast recognition under complex background interference, stability of recognition under different lighting environments for the same crop, the reliance of recognition and localization on prior information in the presence of fruit overlap and occlusions caused by leaves and branches, and the uncertainty of picking caused by complex working environments. In current research on algorithms dealing with complex background interference and various occlusion disturbances, good results have been achieved. Different lighting environments have a significant impact on the recognition and positioning of fruits and vegetables, with a minimum accuracy of 59.2%. Finally, this article outlines future research directions to address these challenges.

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“…Rodriguze et al, developed a 15-degree-of-freedom single-motor-driven multi-finger hand, which can achieve safe and reliable grasping without any sensors and feedback control [31]. At present, most multi-finger hands with bending and torsional complex degrees of freedom and active soft control are used for humanoid dexterous operation, integrating multiple sensors, motors and actuators, with complex control and high cost [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

End-Effectors Developed for Citrus and Other Spherical Crops

Wang

Jiang

2022

Applied Sciences

Self Cite

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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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Fast recognition method for citrus under complex environments based on improved YOLOv3

Cited by 9 publications

References 17 publications

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

End-Effectors Developed for Citrus and Other Spherical Crops

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