System Design of Tomatoes Harvesting Robot Based on Binocular Vision

Zhou, Tao; Zhang, Dong; Zhou, Maodeng; Xi, Houyin; Chen, Xia

doi:10.1109/cac.2018.8623150

Cited by 8 publications

(7 citation statements)

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“…After eliminating the noise with the bilateral filter, the color and texture characteristics of the image were extracted through the RGB value and the grayscale paragenesis matrix of the image and then input into the Least Squares Support Vector Machine model (LS-SVM). The segmentation time of the trained model for the Hangzhou chrysanthemum was as low as 0.7 s. In addition, during tomato fruit picking, Zhou et al [60] used a variable baseline USB binocular camera (HNY-CV-002), combined with the identification method of circular Hough transform and RGB color space, to achieve efficient picking of the target tomato. In addition, Jin et al [61] applied depth learning technology to the binocular camera to identify the target fruit in order to better realize the perception of tomato fruit and achieved good results.…”

Section: Object Perception Based On Binocular Visionmentioning

confidence: 99%

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Ji,

Huang,

Wang

et al. 2023

Agronomy

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Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production.

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Section: Object Perception Based On Binocular Visionmentioning

confidence: 99%

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Ji,

Huang,

Wang

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…Compared with traditional monitoring systems based on measuring point sensors, such as accelerometers, robotic total stations, etc., the application of the image-based monitoring system to estimate the service state of bridge structures has advantages such as holography, convenience, and economy [16][17][18]. Access devices for digital images usually include digital cameras [19][20][21][22][23], UAV cameras [10,[24][25][26], industrial cameras [27][28][29][30], mobile phones [31][32][33], etc. Many researchers [34][35][36] have achieved success in using image processing techniques to detect cracks in concrete surfaces.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Measuring and Tracking Structural Displacement Based on Surveillance Video Image Analysis

Ni,

Wang,

Yin

et al. 2024

Sensors

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The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment.

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“…China has a large tomato-planting area and yield, but there are problems with low tomato sorting capacity, high cost, and low efficiency. The sorting process for fruit crops also relies heavily on manual labor [1][2][3][4]. There are currently three main sorting methods for tomatoes of different qualities:…”

Section: Introductionmentioning

confidence: 99%

Design of a Tomato Sorting Device Based on the Multisine-FSR Composite Measurement

et al. 2023

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The ripeness of tomatoes is crucial to determining their shelf life and quality. Most of the current methods for picking and sorting tomatoes take a long time, so this paper aims to design a device for sorting tomatoes based on force and bioelectrical impedance measurement. A force sensor installed on each of its four fingers may be used as an impedance measurement electrode. When picking tomatoes, the electrical impedance analysis circuit is first connected for pre-grasping. By applying a certain pre-tightening force, the FSR sensor on the end effector finger can be tightly attached to the tomato and establish an electric current pathway. Then, the electrical parameters of the tomato are measured to determine its maturity, and some of the electrical parameters are used for force monitoring compensation. Then, a force analysis is conducted to consider the resistance of the FSR under current stress. According to the principle of complex impedance circuit voltage division, the voltage signal on the tomato is determined. At the same time, the specific value of the grasping force at this time is determined based on the calibration of the pre-experiment and the compensation during the detection process, achieving real-time detection of the grasping force. The bioelectricity parameters of tomatoes can not only judge the ripeness of tomatoes, but also compensate for the force measurement stage to achieve more accurate non-destructive sorting. The experimental results showed that within 0.6 s of stable grasping, this system could complete tomato ripeness detection, improve the overall tomato sorting efficiency, and achieve 95% accuracy in identifying ripeness through impedance.

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System Design of Tomatoes Harvesting Robot Based on Binocular Vision

Cited by 8 publications

References 0 publications

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Experimental Study on Measuring and Tracking Structural Displacement Based on Surveillance Video Image Analysis

Design of a Tomato Sorting Device Based on the Multisine-FSR Composite Measurement

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