scholarly journals SoilCam: A Fully Automated Minirhizotron using Multispectral Imaging for Root Activity Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 787
Author(s):  
Gazi Rahman ◽  
Hanif Sohag ◽  
Rakibul Chowdhury ◽  
Khan A. Wahid ◽  
Anh Dinh ◽  
...  
Keyword(s):  
White Light ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Image Sensor ◽  
Motion Blur ◽  
Image Resolution ◽  
In Situ Monitoring ◽  
Geometric Distortion ◽  
Entire Area

A minirhizotron is an in situ root imaging system that captures components of root system architecture dynamics over time. Commercial minirhizotrons are expensive, limited to white-light imaging, and often need human intervention. The implementation of a minirhizotron needs to be low cost, automated, and customizable to be effective and widely adopted. We present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations. The imaging system is multi-modal, i.e., it supports both conventional white-light and multispectral imaging, with fully automated operations for long-term in-situ monitoring using wireless control and access. The system is capable of taking 360° images covering the entire area surrounding the tube. The image sensor can be customized depending on the spectral imaging requirements. The maximum achievable image quality of the system is 8 MP (Mega Pixel)/picture, which is equivalent to a 2500 DPI (dots per inch) image resolution. The length of time in the field can be extended with a rechargeable battery and solar panel connectivity. Offline image-processing software, with several image enhancement algorithms to eliminate motion blur and geometric distortion and to reconstruct the 360° panoramic view, is also presented. The system is tested in the field by imaging canola roots to show the performance advantages over commercial systems.

scholarly journals A Cost-Effective In Situ Zooplankton Monitoring System Based on Novel Illumination Optimization

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3471
Author(s):  
Zhiqiang Du ◽  
Chunlei Xia ◽  
Longwen Fu ◽  
Nan Zhang ◽  
Bowei Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Imaging System ◽  
Physiological State ◽  
Cost Effective ◽  
In Situ Monitoring ◽  
Measuring System ◽  
Target Area ◽  
Image Processing Algorithm ◽  
Led Array

A cost-effective and low-power-consumption underwater microscopic imaging system was developed to capture high-resolution zooplankton images in real-time. In this work, dark-field imaging was adopted to reduce backscattering and background noise. To produce an accurate illumination, a novel illumination optimization scheme for the light-emitting diode (LED) array was proposed and applied to design a lighting system for the underwater optical imaging of zooplankton. A multiple objective genetic algorithm was utilized to find the best location of the LED array, which resulted in the specific illumination level and most homogeneous irradiance in the target area. The zooplankton imaging system developed with the optimal configuration of LEDs was tested with Daphnia magna under laboratory conditions. The maximal field of view was 16 mm × 13 mm and the optical resolution was 15 μm. The experimental results showed that the imaging system developed could capture high-resolution and high-definition images of Daphnia. Subsequently, Daphnia individuals were accurately segmented and their geometrical characters were measured by using a classical image processing algorithm. This work provides a cost-effective zooplankton measuring system based on an optimization illumination configuration of an LED array, which has a great potential for minimizing the investment and operating costs associated with long-term in situ monitoring of the physiological state and population conditions of zooplankton.

scholarly journals A Practical One-Shot Multispectral Imaging System Using a Single Image Sensor

2015 ◽  
Vol 24 (10) ◽  
pp. 3048-3059 ◽  
Author(s):  
Yusuke Monno ◽  
Sunao Kikuchi ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Image Sensor ◽  
Single Image

A simple null-field ellipsometric imaging system (NEIS) for in-situ monitoring of EUV-induced deposition on EUV optics

2010 ◽  
Author(s):  
Rashi Garg ◽  
Nadir Faradzhev ◽  
Shannon Hill ◽  
Lee Richter ◽  
P. S. Shaw ◽  
...  
Keyword(s):  
Imaging System ◽  
In Situ Monitoring ◽  
Null Field

Multispectral Imaging for Automated Tissue Identification of Normal Human Surgical Specimens

2020 ◽  
pp. 019459982094101
Author(s):  
Jared A. Shenson ◽  
George S. Liu ◽  
Joyce Farrell ◽  
Nikolas H. Blevins
Keyword(s):  
White Light ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Human Head ◽  
Color Images ◽  
Operating Microscope ◽  
Tissue Identification ◽  
Surgical Residents ◽  
Normal Human ◽  
Light Color

Objective Safe surgery requires the accurate discrimination of tissue intraoperatively. We assess the feasibility of using multispectral imaging and deep learning to enhance surgical vision by automated identification of normal human head and neck tissues. Study Design Construction and feasibility testing of novel multispectral imaging system for surgery. Setting Academic university hospital. Subjects and Methods Multispectral images of fresh-preserved human cadaveric tissues were captured with our adapted digital operating microscope. Eleven tissue types were sampled, each sequentially exposed to 6 lighting conditions. Two convolutional neural network machine learning models were developed to classify tissues based on multispectral and white-light color images (ARRInet-M and ARRInet-W, respectively). Blinded otolaryngology residents were asked to identify tissue specimens from white-light color images, and their performance was compared with that of the ARRInet models. Results A novel multispectral imaging system was developed with minimal adaptation to an existing digital operating microscope. With 81.8% accuracy in tissue identification of full-size images, the multispectral ARRInet-M classifier outperformed the white-light-only ARRInet-W model (45.5%) and surgical residents (69.7%). Challenges with discrimination occurred with parotid vs fat and blood vessels vs nerve. Conclusions A deep learning model using multispectral imaging outperformed a similar model and surgical residents using traditional white-light imaging at the task of classifying normal human head and neck tissue ex vivo. These results suggest that multispectral imaging can enhance surgical vision and augment surgeons’ ability to identify tissues during a procedure.

An In Situ Multispectral Imaging System for Planar Optodes in Sediments: Examples of High-Resolution Seasonal Patterns of pH

2011 ◽  
Vol 17 (4-5) ◽  
pp. 457-471 ◽  
Author(s):  
Yanzhen Fan ◽  
Qingzhi Zhu ◽  
Robert C. Aller ◽  
Donald C. Rhoads
Keyword(s):  
High Resolution ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Seasonal Patterns

In situ monitoring of 3D in vitro cell aggregation using an optical imaging system

2008 ◽  
Vol 100 (1) ◽  
pp. 159-167 ◽  
Author(s):  
N.B.E. Sawyer ◽  
L.K. Worrall ◽  
J.A. Crowe ◽  
S.L. Waters ◽  
K.M. Shakesheff ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Imaging System ◽  
Cell Aggregation ◽  
In Situ Monitoring ◽  
Optical Imaging System
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