Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry

2020 ◽  
Vol 67 (1) ◽  
pp. 133-141
Author(s):  
Dmitriy O. Khort ◽  
Aleksei I. Kutyrev ◽  
Igor G. Smirnov ◽  
Rostislav A. Filippov ◽  
Roman V. Vershinin
Keyword(s):  
Computer Vision ◽  
Technological Process ◽  
Degrees Of Freedom ◽  
Computer Control ◽  
Recognition System ◽  
Raspberry Pi ◽  
Laser Sensors ◽  
Lighting Conditions ◽  
Systems Research ◽  
Degree Of Maturity

Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.

scholarly journals Device for robotic picking of strawberries

2020 ◽  
Vol 193 ◽  
pp. 01045
Author(s):  
D. O. Khort ◽  
A. I. Kutyrev ◽  
R. A. Filippov ◽  
R. V. Vershinin
Keyword(s):  
Computer Vision ◽  
Degrees Of Freedom ◽  
3D Model ◽  
Field Experiments ◽  
Vision System ◽  
Load Capacity ◽  
Size Factor ◽  
Computer Vision System ◽  
Lighting Conditions ◽  
Maximum Effort

The article presents a device for collecting strawberry berries, presents a 3D model of an automated manipulator with a computer vision system. The overall dimensions of the manipulator (WxDxH), mm - 200x700x600, the number of degrees of freedom - 6, load capacity - 1 kg, the maximum effort to detach fruits and berries - 10-80 N. An algorithm for the operation of a computer vision system for determining the boundaries of berries and their ripeness based on size factor. The results of field experiments, measuring the area of berries of wild strawberries at different times of the day are given. It was established that the lighting conditions have a significant effect on the detection of the boundaries and area of garden strawberries, and their ripeness.

scholarly journals Development of Hexapod Robot with Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 1796-1805
Author(s):  
Abhishek C
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Digital Image Processing ◽  
Digital Image ◽  
Degrees Of Freedom ◽  
Legged Locomotion ◽  
Robotic Systems ◽  
Raspberry Pi ◽  
Hexapod Robot ◽  
Irregular Surfaces

Abstract: Nowadays many robotic systems are developed with lot of innovation, seeking to get flexibility and efficiency of biological systems. Hexapod Robot is the best example for such robots, it is a six-legged robot whose walking movements try to imitate the movements of the insects, it has two sets of three legs alternatively which is used to walk, this will provide stability, flexibility and mobility to travel on irregular surfaces. With these attributes the hexapod robots can be used to explore irregular surfaces, inhospitable places, or places which are difficult for humans to access. This paper involves the development of hexapod robot with digital image processing implemented on Raspberry Pi, to study in the areas of robotic systems with legged locomotion and robotic vision. This paper is an integration of a robotic system and an embedded system of digital image processing, programmed in high level language using Python. It is equipped with a camera to capture real time video and uses a distance sensor that allow the robot to detect obstacles. The Robot is Self-Stabilizing and can detect corners. The robot has 3 degrees of freedom in each six legs thus making a 18 DOF robotic movement. The use of multiple degrees of freedom at the joints of the legs allows the legged robots to change their movement direction without slippage. Additionally, it is possible to change the height from the ground, introducing a damping and a decoupling between the terrain irregularities and the body of the robot servo motors. Keywords: Hexapod, Raspberry Pi, Computer vision, Object detection, Yolo, Servo Motor, OpevCV.

scholarly journals Automatic Number Plate Recognition System using Raspberry Pi

2020 ◽  
Vol 9 (2) ◽  
pp. 1863-1865
Keyword(s):  
Computer Vision ◽  
Access Control ◽  
Embedded System ◽  
Recognition System ◽  
Binary Image ◽  
Security System ◽  
Raspberry Pi ◽  
Gray Scale

Automatic Number Plate Recognition System is an embedded system that acknowledges the vehicle number plate automatically. Automatic Number Plate Recognition is a technology for computer vision to find the number plates of vehicles from the images. There are many applications like parking, access control, security system, etc. In this paper, we propose a technique of implementing Automatic Number Plate Recognition System using Python and Open Computer Vision Library. The different stages that are involved in the implementation are conversion into gray scale, conversion into binary image, detects the edges of the image, to find the contours and finally displays the number plate of a vehicle

Computer Control with Hand Gestures Using Computer Vision

2020 ◽  
Author(s):  
Nirmala J S ◽  
Ajeet Kumar ◽  
Adith Jose E A ◽  
Kapil Kumar ◽  
Abhishek R Malvadkar
Keyword(s):  
Computer Vision ◽  
Computer Control ◽  
Hand Gestures

scholarly journals Jan van Eyck's Perspectival System Elucidated Through Computer Vision

2021 ◽  
Vol 4 (2) ◽  
pp. 1-8
Author(s):  
Gilles Simon
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
3D Reconstruction ◽  
Degrees Of Freedom ◽  
Human Vision ◽  
Vanishing Points ◽  
Two Degrees Of Freedom ◽  
Creative Media

It is generally accepted that Jan van Eyck was unaware of perspective. However, an a-contrario analysis of the vanishing points in five of his paintings, realized between 1432 and 1439, unveils a recurring fishbone-like pattern that could only emerge from the use of a polyscopic perspective machine with two degrees of freedom. A 3D reconstruction of Arnolfini Portrait compliant with this pattern suggests that van Eyck's device answered a both aesthetic and scientific questioning on how to represent space as closely as possible to human vision. This discovery makes van Eyck the father of today's immersive and nomadic creative media such as augmented reality and synthetic holography.

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

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