scholarly journals Estimating Morphological Features of Plant Growth Using Machine Vision

2019 ◽  
Vol 10 (3) ◽  
pp. 30-53
Author(s):  
Himanshu Gupta ◽  
Roop Pahuja
Keyword(s):  
Plant Growth ◽  
Machine Vision ◽  
Real Time ◽  
Growth Parameters ◽  
Vision System ◽  
Digital Camera ◽  
Vital Role ◽  
Morphological Features ◽  
Virtual Instrumentation ◽  
Effective Use

Motivated by the fact that human visionary intelligence plays a vital role in guiding many of the agriculture practices, this article represents an effective use of machine vision technology for estimating plant morphological features to ascertain its growth and health conditions. An alternative to traditional, manual and time-consuming testing methods of plant growth parameters, a novel online plant vision system is proposed and developed on the platform of virtual instrumentation. Deployed in real time, the system acquires plant images using digital camera and communicates the raw image to host PC on Wi-Fi network. The dedicated application software with plant user interface, effective image processing and analysis algorithms, loads the plant images, extracts and estimates certain morphological features of the plant such as plant height, leaf area, detection of flower onset and fall foliage. The system was tested and validated under real-time conditions using different plants and leaves. Further, the performance of the system was statistically analysed to show promising results.

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

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 Real-Time Machine Vision System for an Automated Quality Monitoring in Mass Production of Multiaxial Non-Crimp Fabrics

2015 ◽  
Vol 48 (3) ◽  
pp. 2393-2398 ◽  
Author(s):  
R. Schmitt ◽  
T. Fürtjes ◽  
B. Abbas ◽  
P. Abel ◽  
W. Kimmelmann ◽  
...  
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Mass Production ◽  
Vision System ◽  
Quality Monitoring ◽  
Machine Vision System ◽  
Time Machine

Machine Vision Recognition of Weld Pool in Gas Tungsten Arc Welding

1995 ◽  
Vol 209 (2) ◽  
pp. 141-152 ◽  
Author(s):  
R Kovacevic ◽  
Y M Zhang
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Weld Pool ◽  
Visual Information ◽  
Vision System ◽  
Tracking Error ◽  
Seam Tracking ◽  
Front Face ◽  
Welding Parameters ◽  
Weld Penetration

The weld pool and its surrounding area can provide a human welder with sufficient visual information to control welding quality. Seam tracking error and pool geometry can be recognized by a skilled human welder and then utilized to adjust the welding parameters. However, for machine vision, accurate real-time recognition of weld pool geometry is a difficult task due to the high intensity arc light, even though seam tracking errors can be detected. A novel vision system is, therefore, used to acquire quality images against the arc. A real-time recognition algorithm is proposed to analyse the image and recognize the pool geometry based on the pattern recognition technique. Despite surface impurity and other influences, the pool geometry can always be recognized with sufficient accuracy in 150 ms under different welding conditions. To explore the potential application of machine vision in weld penetration control, experiments are conducted to show the correlation between pool geometry and weld penetration state. Thus, pool recognition also provides a possible technique for front-face sensing of the weld penetration.

scholarly journals Comparison of Three Smart Camera Architectures for Real-Time Machine Vision System

10.5772/57135 ◽  
2013 ◽  
Vol 10 (12) ◽  
pp. 402 ◽  
Author(s):  
Abdul Waheed Malik ◽  
Benny Thörnberg ◽  
Prasanna Kumar
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Smart Camera ◽  
Time Machine
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