Geometrical and morphological image processing algorithm

1991 ◽  
Author(s):  
Kamran Reihani ◽  
Wiley E. Thompson
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Morphological Image Processing ◽  
Morphological Image

scholarly journals A Study on Modular Smart Plant Factory Using Morphological Image Processing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1661
Author(s):  
Bong-Hyun Kim ◽  
Joon-Ho Cho
Keyword(s):  
Image Processing ◽  
Control System ◽  
Plant Growth ◽  
High Efficiency ◽  
Control Method ◽  
Image Processing Algorithm ◽  
Solar Module ◽  
Morphological Image Processing ◽  
Plant Factory ◽  
Morphological Image

This paper is a study on a modular smart plant factory integrating intelligent solar module, LED module with high efficiency for plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the plant factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on plant growth and status was confirmed.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

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