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.

Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

2021 ◽  
pp. 004051752110342
Author(s):  
Sifundvolesihle Dlamini ◽  
Chih-Yuan Kao ◽  
Shun-Lian Su ◽  
Chung-Feng Jeffrey Kuo
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection System ◽  
Vision System ◽  
Good Precision ◽  
Machine Vision System ◽  
Time Machine ◽  
Textile Fabric

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

scholarly journals Machine vision system: a tool for quality inspection of food and agricultural products

2011 ◽  
Vol 49 (2) ◽  
pp. 123-141 ◽  
Author(s):  
Krishna Kumar Patel ◽  
A. Kar ◽  
S. N. Jha ◽  
M. A. Khan
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Agricultural Products ◽  
Quality Inspection ◽  
Machine Vision System ◽  
System A

Research of Machine Vision System about Robot Soccer Based on the HSV

2010 ◽  
Vol 121-122 ◽  
pp. 807-812
Author(s):  
Sheng Rong Lu ◽  
Huan Long Guo
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Process Design ◽  
Vision System ◽  
Processing Technology ◽  
Machine Vision System ◽  
Identification Process ◽  
Image Access ◽  
Processing Factors ◽  
Rgb Image

In this paper, including the introduction of image processing and image processing factors influence the discussion, after the RGB image access to the transformation of HSV, mainly through the robot to select the image of the identification process design, completed the capture of a robot A specific color of the entity, after the robot moves to give you accurate basis for the judgment. First of all, the introduction for images on the basis of the relevant knowledge is presented. Second, the image and the image of the divisions are briefly introduced. Third, we proposed the image factors and image processing technology. Fourth, conversion from RGB to HSV model is presented. Finally, we designed the image of the robot to access and identify procedures modular.

scholarly journals Measurements of Tool Wear Parameters Using Machine Vision System

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Avinash A. Thakre ◽  
Aniruddha V. Lad ◽  
Kiran Mala
Keyword(s):  
Image Processing ◽  
Tool Wear ◽  
Machine Vision ◽  
Vision System ◽  
Average Error ◽  
Scanning Electron Micrographs ◽  
Machine Vision System ◽  
Machining Time ◽  
Wear Area ◽  
Carbide Inserts

Monitoring tool wear is very important in machining industry as it may result in loss of dimensional accuracy and quality of finished product. This work includes the development of machine vision system for the direct measurement of flank wear of carbide cutting tool inserts. This system consists of a digital camera to capture the tool wear image, a good light source to illuminate the tool, and a computer for image processing. A new approach of inline automatic calibration of a pixel is proposed in this work. The captured images of carbide inserts are processed, and the segmented tool wear zone has been obtained by image processing. The vision system extracts tool wear parameters such as average tool wear width, tool wear area, and tool wear perimeter. The results of the average tool wear width obtained from the vision system are experimentally validated with those obtained from the digital microscope. An average error of 3% was found for measurements of all 12 carbide inserts. Scanning electron micrographs of the wear zone indicate the severe abrasion marks and damage to the cutting edge for higher machining time. This study indicates that the efficient and reliable vision system can be developed to measure the tool wear parameters.

Image processing in dimensional measurement for hot large forgings based on laser-aided binocular machine vision system

2016 ◽  
Author(s):  
Chaonan Fan ◽  
Wei Liu ◽  
Pengtao Xu ◽  
Yang Liu ◽  
Jinghao Yang ◽  
...  
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Dimensional Measurement
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