scholarly journals The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3933
Author(s):  
Sławomir Kurpaska ◽  
Andrzej Bielecki ◽  
Zygmunt Sobol ◽  
Marzena Bielecka ◽  
Magdalena Habrat ◽  
...  
Keyword(s):  
Detection System ◽  
Vision System ◽  
Video Object ◽  
Strawberry Fruit ◽  
Final Phase ◽  
System A ◽  
Correct Location ◽  
Traction System ◽  
Working Section

Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with four subsystems: a video object detection system, a collecting arm, a unit for the reception and possible packaging of the fruit, and a traction system unit. This paper presents a concept for the design and operation of the working section of a harvester for strawberry fruit crops grown in rows or beds, in open fields, and/or under cover. In principle, the working section of the combine should meet parameters comparable with those of manually harvested strawberries (efficiency, quality of harvested fruit) and minimise contamination in the harvested product. In order to meet these requirements, in the presented design concept, it was assumed that these activities would be performed during harvesting with the natural distribution of fruits within the strawberry bush, and the operation of the working head arm maneuvering in the vicinity of the picked fruit, the fruit receiving unit, and other obstacles was developed on the basis of image analysis, initially general, and in detail in the final phase. The paper also discusses the idea of a vision system in which the algorithm used has been positively tested to identify the shapes of objects, and due to the similarity of space, it can be successfully used for the correct location of strawberry fruit.

scholarly journals Study on Machine Learning Based Intelligent Defect Detection System

2018 ◽  
Vol 201 ◽  
pp. 01010 ◽  
Author(s):  
Chung-Chi Huang ◽  
Xin-Pu Lin
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Detection System ◽  
Vision System ◽  
National Development ◽  
Image Preprocessing ◽  
Testing Procedures ◽  
Metal Products

In the paper, it is proposed to develop a machine learning based intelligent defect detection system for metal products. The common machine vision system has the surface (stain, shallow pit, shallow tumor, scratches, Edge defects, pattern defects) detection, or for the processing of the size, diameter, diameter, eccentricity, height, thickness and other parts of the non-contact numerical parameters of detection. Considering the quality of the work piece and the defects of the standard, so for the quality of customized testing requirements, the study is the development of machine vision and machine learning metal products defect detection system, mainly composed of three procedures: Image preprocessing, training procedures and testing procedures. The system architecture consists of three parts: (1) Image preprocessing: we first use the machine vision. OPENCV to carry out the image pre-processing part of the product before the detection. (2) Training procedures: The algorithm of the machine learning includes the convolution neural network (CNN), chunk-max pooling is used to train the program, and the generative adversarial network (GAN) based architecture is used to solve the problem of small datasets for surface defects. (3) Testing procedures：The Python language is used to write the program and implement the testing procedures with the GPU-Based embedded hardware In industries, collecting training dataset is usually costly and related methods are highly dataset-dependent. So most companies cannot provide Big-data to be analyzed or applied. By the experimental results, the recognition accuracy can be obviously improved as increasing data augmentation by GAN-Based samples maker. Manual inspection is labor intensive, costly and less in efficiency. Therefore, this study will contribute to technological innovation, industry, national development and other applications. (1) The use of intelligent machine learning technology will make the industry 4.0 technology more sophisticated. (2) It will make the development of equipment industry be better by the machine learning applications. (3) It will increase the economics and productivity of countries for the aging of the population by machine learning.

scholarly journals Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

2021 ◽  
Author(s):  
Jiarui Xie
Keyword(s):  
Real Time ◽  
Detection System ◽  
Region Of Interest ◽  
Cost Effective ◽  
Time Warping ◽  
Time Data ◽  
Fused Filament Fabrication ◽  
System A ◽  
Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

Computer vision system: a tool for evaluating the quality of wheat in a grain tank

2018 ◽  
Author(s):  
Ivan Konovalenko ◽  
Aleksandr Shkanaev ◽  
Uryi Minkin ◽  
Aleksei Panchenko ◽  
Dmitry Putintsev ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
System A

scholarly journals The concept of a construction solution of a robot for harvesting strawberries

2019 ◽  
Vol 132 ◽  
pp. 01019 ◽  
Author(s):  
Piotr Nawara ◽  
Norbert Pedryc ◽  
Zygmunt Sobol ◽  
Sławomir Kurpaska ◽  
Dariusz Baran
Keyword(s):  
Image Analysis ◽  
Mechanical Strength ◽  
Work Productivity ◽  
Strawberry Fruit ◽  
Natural Distribution ◽  
Working Space ◽  
Working Section ◽  
Consumption Value

Strawberry fruit products are of high commercial and consumption value, while difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the plants. Therefore, robots harvesting strawberries should connect four subsystems: vision of detection, delivery arm (manipulator), effector (harvesting head), and finally- a platform increasing the working space adapted to the size of the farm. The presented work of the conceptual working section of a combine for harvesting strawberry fruit from crops, carried out in rows or cultivation ridge, from cultivation on field and/ or under covers will meet the requirements for: work productivity, quality of harvested fruit, reduction of the amount of pollution. To requirements have been met, the developed concept of constructions adopted the principle of operation during the first phase of the harvesting (in the natural distribution of fruit within the plants of strawberries) and the working of the work arm head (based on image analysis, initially general, and in the last phase of detailed) maneuvering in surrounded by harvested fruit and machine.

Design and Control of an Omnibot Autonomous Vehicle

2008 ◽  
Author(s):  
Steven Bemis ◽  
Brian Riess ◽  
Scott Nokleby
Keyword(s):  
Collision Detection ◽  
Closed Loop ◽  
Detection System ◽  
Vision System ◽  
Autonomous Vehicle ◽  
Robotic Arm ◽  
Mobile Manipulator ◽  
Data Routing ◽  
System A ◽  
And Control

A design for autonomous control of a novel omni-directional platform is presented. This platform is to be used in conjunction with a robotic arm to further research of mobile-manipulator systems. This design differs from other omni-directional platforms that use omniwheels in that its drive axes do not intersect its geometric centre. The platform can be controlled autonomously through multiple sub-systems that have been designed including a closed-loop velocity controller, a localization system, an obstacle avoidance and collision detection system, a vision system, and a data routing system. These sub-systems communicate with a remote computer which plots the path and sends data to guide the platform. The closed-loop velocity controller provides feedback which can be used to analyze and correct the path of travel.

scholarly journals Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1027
Author(s):  
Md Sultan Mahmud ◽  
Qamar U. Zaman ◽  
Travis J. Esau ◽  
Young K. Chang ◽  
G. W. Price ◽  
...  
Keyword(s):  
Image Processing ◽  
Powdery Mildew ◽  
Machine Vision ◽  
Detection System ◽  
Vision System ◽  
Machine Vision System ◽  
Field Site ◽  
System A ◽  
Processing Program ◽  
Powdery Mildew Disease

Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

Technology and Hospital Information System: A Case of Implementation of Electronic Clinical Records in Hospital Maternity Irene Neto in Lubango, Angola

2019 ◽  
Vol 10 (11) ◽  
pp. 1131-1135
Author(s):  
Tomas Hambili Paulo Sanjuluca ◽  
◽  
Ricardo Correia ◽  
Anabela Antunes de Almeida ◽  
Ana Gloria Diaz Martinez ◽  
...  
Keyword(s):  
Maternity Hospital ◽  
Management Support ◽  
Retrospective Case ◽  
System A ◽  
Clinical Records ◽  
Retrospective Case Study ◽  
The Impact ◽  
And Child Health

Introduction: In order to have a good assessment of the quality of maternal and child health care, it is essential that there is up-to-date and reliable information. Objective: To evaluate the impact of the implementation of a computerized database of clinical processes in the admission, archive and medical statistics section, of Maternity hospital Irene Neto/Lubango-Angola. Methodology: A descriptive study with a quantitative and qualitative approach to carry out a retrospective case study deliveries and newborns, records from 2014 to 2017. Final considerations: The implementation of this project may contribute to the improvement of clinical management support management of the hospital as well as facilitating access to information for research and scientific production.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

scholarly journals Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 791
Author(s):  
Sufei Zhang ◽  
Ying Guo
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Vision System ◽  
Expert Group ◽  
Group Test ◽  
System A ◽  
Real Objects ◽  
Practical Tool ◽  
Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

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