IQ-ThermoPortable hyperspectral imaging and infrared thermal imaging system

This system, with its characteristics of portability, lightweight, intelligence, ready to use, online measurement, and real-time analysis, is widely applicable to various scenarios such as laboratories or fields. By high-resolution imaging of leaf or canopy horizontal spectral reflectance and temperature, it can be applied to rapid non-destructive, high-throughput in-situ ecological remote sensing monitoring, vegetation biotic and abiotic stress monitoring, plant transpiration and stomatal conductance research, biodiversity monitoring, etc., especially for leaf and canopy scale vegetation growth monitoring, species diversity investigation, environmental and ecological system dynamic changes, etc. It is of great significance.

This system mainly consists of a spectral imaging sensor and a portable platform. The imaging sensor includes a built-in push scan intelligent hyperspectral imaging unit and an LWIR infrared thermal imaging unit. The hyperspectral imaging unit integrates functions such as acquisition, analysis and processing, and result visualization (ALL-IN-ONE), with IP level protection and fully automatic operation characteristics. It has built-in WiFi for remote control, enabling unmanned aerial vehicle duty work. Has won the 2018 German Design Association's "Red Dot Design Award" - an internationally recognized top global industrial design award, and has won the "inVISION Global Top Creative Award" for two consecutive years. The infrared thermal imaging unit has a pixel resolution of up to 640 × 512px and ultra-high sensitivity of 0.03 ℃. Its low energy consumption, lightweight, and robust structural design are perfectly suitable for in-situ monitoring scenarios in complex and harsh outdoor conditions.

Application areas:

Suitable for photosynthesis research, vegetation stress research, agriculture, forestry, ecosystem monitoring and other fields. The research content involves photosynthetic activity, stress response, pest and disease monitoring, farmland mapping and census, etc

ü Field in-situ ecological remote sensing monitoring

ü Monitoring and control of pests and diseases

ü Forest resource survey and assessment

ü High throughput remote sensing monitoring of sample plots

ü Research on Plant Phenotype and Morphology

ü Crop yield assessment and agricultural monitoring

ü Monitoring of Crop Drought Stress and Irrigation Management

ü Farmland surveying and agricultural census

ü Crop breeding and resistance screening

ü Biodiversity and germplasm resources survey

Analysis of crop canopy temperature

Functional Features

§ Systematic and integrated design, lightweight and portable, suitable for field in situ ecological surveys

§ Intelligent hyperspectral imaging sensor, covering the 400-1000nm wavelength range, capable of calculating dozens of vegetation index images

§ High performance infrared thermal imaging temperature measurement system,Temperature resolution of 0.03 ℃, equipped with professional temperature data analysis software, extracting dynamic temperature change curves of the area of interest

§ The hyperspectral imaging sensor is equipped with a GPS module, which facilitates the fusion and analysis of data from different geographical locations

Main technical indicators:

1 Systematic bracket design: integrating full solar spectrum dual light sources, imaging unit, gimbal, and tripod, weighing about 5kg, portable assembly, and easy operation

2 400-1000nmIntelligent hyperspectral imaging: integrates functions such as spectral data acquisition, automatic scanning imaging, automatic analysis and processing, and visualization of analysis results. It can be directly applied to cameras through the creation of spectral characteristic curve apps for rapid screening, detection, and recognition of traits

a) Aperture F/1.7

b) Spectral resolution of 7nm

c) Spectral band: 204, optional Bin 2x and Bin 3x

d) Built in GPS, each hyperspectral data cube comes with a geotag for precise positioning and multi-source information fusion analysis

e) Built in SAM algorithm, without any complex processing, can quickly display analysis results in real time

f) Equipped with a 4.3-inch touch screen and 13 physical buttons, it can quickly measure and analyze results in real-time

g) Equipped with USB or WIFI remote control function, the camera can be controlled to run through software via USB cable or wireless WIFI

3、 7.5-13.5 μ m infrared thermal imaging, uncooled infrared focal plane detector, 640 × 512 pixels, factory black body calibration, built-in NUC calibration, including calibration certificate, temperature resolution 0.03 ℃, 9/30/60Hz optional

a) Temperature measurement range: -25 ℃ to+150 ℃ or+40 ℃ to+550 ℃, optional 1500 ℃

b) Temperature sensitivity ≤ 0.03 ℃ (30mK) @ 30 ℃;

c) Data transmission: USB-3 or GigE Gigabit Ethernet

d) Optical lens, optional 6.8mm, 9mm, 13mm, 19mm lenses

e) Equipped with 14 color palettes for arbitrary selection, allowing for diverse settings of thermal imaging false colors

f) Equipped with isothermal mode, temperature warning, ROI analysis, temperature profile, 3D temperature display, output report and other functions

g) Supports CSV, non radiative JPEG, radiative JPEG, radiative video, AVI, MP4 and other formats for output

h) Protection level: IP65， Suitable for harsh outdoor conditions

Outdoor use photos

Installation training

Screenshot of thermal imaging software (left) Screenshot of hyperspectral data analysis (right)

Hyperspectral analysis for Arabidopsis phenotype analysis (case study)

reference:

1) Jan B , Kelvin A , Dzhaner E , et al. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection[J]. Sensors, 2018, 18(2):441-.

2) Xiao Z , Wang J . Rapid Nondestructive Defect Detection of Scindapsus aureus Leaves Based on PCA Spectral Feature Optimization[J]. IOP Conference Series Earth and Environmental Science, 2020, 440:032018.

3) Detection of Diseases on Wheat Crops by Hyperspectral Data

4) Barreto, Abel & Paulus, Stefan & Varrelmann, Mark & Mahlein, Anne-Katrin. (2020). Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms. Journal of Plant Diseases and Protection. 10.1007/s41348-020-00344-8.

5) Sajad Kiani, Saskia M. van Ruth, Leo W.D. van Raamsdonk, Saeid Minaei. Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study. LWT - Food Science and Technology. 104(2019)61-69.

6) Edelman, G.J. & Aalders, M.C.G. (2018). Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes. Forensic Science International. 292. 10.1016/j.forsciint.2018.09.025.

7) Yuan, X.; Laakso, K.; Davis, C.D.; Guzmán Q., J.A.; Meng, Q.; Sanchez-Azofeifa, A. Monitoring the Water Stress of an Indoor Living Wall System Using the “Triangle Method”. Sensors 2020, 20, 3261.

8) Kruglikov, N. & Danilenko, I. & Muftakhetdinova, Razilia & Petrova, Evgeniya & Grokhovsky, V.. (2019). Spectral characteristics of the meteoritic material after the modeling of thermal and shock metamorphism. AIP Conference Proceedings. 2174. 020227. 10.1063/1.5134378.