A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot

Lv, Jidong; Wang, Yijie; Xu, Liming; Gu, Yuwan; Zou, Ling; Yang, Biao; Ma, Zhenghua

doi:10.1016/j.scienta.2019.108758

Cited by 33 publications

(20 citation statements)

References 7 publications

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“…It is obvious that farm robots will assist future farmers in many aspects. Multiple researchers have focused on developing a new robotic system to plant, protect, and harvest a variety of crops, such as apple (Lv et al, 2019), orange (Garner et al, 2006), tomato (Li et al, 2019, strawberry (Xiong et al, 2019;Aliasgarian et al, 2015), kiwifruit (Williams et al, 2019), grape (Luo et al, 2018), heavy-weight crops (Roshanianfard et al, 2018;Kamata et al, 2018;Noguchi, 2017, 2018b;, and pumpkin Noguchi, 2016, 2018a).…”

Section: Ali Roshanianfard Du Mengmeng Samira Nematzadehlmentioning

confidence: 99%

A 4-DOF SCARA Robotic Arm for Various Farm Applications: Designing, Kinematic Modelling, and Parameterization

Roshanianfard

Nematzadeh

2021

Acta Technologica Agriculturae

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The agriculture industry has faced various challenges nowadays. This research is the first part of a project that presents the designing process, kinematic modelling, and parameterization of a 4-DOF SCARA-type robotic arm specifically designed for work in an agricultural field in terms of seeding, watering, fertilizing, weeding, harvesting, and transporting. The designing of parameters, such as optimum degrees of freedom and component configuration, was done. The kinematic model was calculated using the Denavit-Hartenberg method. The structure of robot was developed for inertia reduction, smooth motion, and torque minimization. The results show that the working space, maximum front access, and side access of developed robotic arm were 11.4 m2, 2.9 m, and 2.4 m, respectively. The results indicate that the robot has sufficient surface coverage for defined farm work.

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Section: Ali Roshanianfard Du Mengmeng Samira Nematzadehlmentioning

confidence: 99%

A 4-DOF SCARA Robotic Arm for Various Farm Applications: Designing, Kinematic Modelling, and Parameterization

Roshanianfard

Nematzadeh

2021

Acta Technologica Agriculturae

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“…Here, we can get the following equations from equation 1, (2) and (3), shown at the bottom of this page.…”

Section: B Kinematics Analysis Of the Manipulator 1) Forward Kinematmentioning

confidence: 99%

Design and Test of a Sorting Device Based on Machine Vision

Zhou

Meng

et al. 2020

IEEE Access

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At present, the sorting of agricultural products in China mainly relies on manual labour, which results in low efficiency, and the development of corresponding automatic equipment lags behind. The grasping method based on machine vision has been widely used in industry, and can provide a reference for the automatic sorting of agricultural products. In this paper, an automatic sorting device for agricultural products was designed. The grasping mechanism adopted a 4-degree-of-freedom (4-DOF) manipulator, and the machine vision control system adopted a monocular camera, which can realize the positioning and classification of the grasp-target. First, the geometric model of the manipulator was established, and the kinematics model of the manipulator was established via the Denavit-Hartenberg (D-H) parameter method. Next, the kinematics analysis and verification were carried out. Then, Zhang Zhengyou calibration method was used to calibrate the camera. An image processing method based on histogram correction was proposed. Based on this, a target positioning algorithm based on the pinhole imaging principle and a target classification algorithm based on the area threshold were established. Finally, an automatic sorting test platform for agricultural products using a visual servo was built. Target classification, positioning and sorting tests were conducted using tomatoes and oranges as the test objects. The test results show that the success rate of the target positioning is close to 98%, that of the target classification is close to 98% and that of the grasping is close to 95%. Furthermore, the sorting time of a single target object can be as fast as 1 second, which can meet the requirements of automatic sorting for common agricultural products. The automatic sorting device for agricultural products has a simple structure, reliable performance and low costs. The structure and algorithms proposed in this paper are simple, reliable, and highly efficient and thus can easily realize technology transplantation. These relevant methods provide a theoretical reference for the development of an automatic sorting device for agricultural products.

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“…The image can be segmented by formula 1, which means that the image itself has no extreme conditions, that is, the gray value difference between the pupil and iris is relatively normal. The image noise interference is removed through the open operation [29], and a grayscale image with pupils is obtained as the eye discrimination image.…”

Section: Eye Concept Knowledge Base and Eye Inferencementioning

confidence: 99%

Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu

Zhu

et al. 2020

Sensors

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Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

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A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot

Cited by 33 publications

References 7 publications

A 4-DOF SCARA Robotic Arm for Various Farm Applications: Designing, Kinematic Modelling, and Parameterization

A 4-DOF SCARA Robotic Arm for Various Farm Applications: Designing, Kinematic Modelling, and Parameterization

Design and Test of a Sorting Device Based on Machine Vision

Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

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