Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art

2018 ◽  
Vol 56 (1) ◽  
pp. 535-558 ◽  
Author(s):  
A.-K. Mahlein ◽  
M.T. Kuska ◽  
J. Behmann ◽  
G. Polder ◽  
A. Walter
Keyword(s):  
Plant Pathogen ◽  
Precision Agriculture ◽  
Plant Protection ◽  
Imaging Techniques ◽  
Plant Diseases ◽  
Plant Phenotyping ◽  
Protection Measures ◽  
Imaging Technologies ◽  
Hyperspectral Sensors ◽  
Plant Pathogen Interactions

Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.

Noninvasive imaging technologies in plant phenotyping

2021 ◽  
Author(s):  
Viet D. Nguyen ◽  
Rijad Sarić ◽  
Timothy Burge ◽  
Oliver Berkowitz ◽  
Martin Trtilek ◽  
...  
Keyword(s):  
Noninvasive Imaging ◽  
Plant Phenotyping ◽  
Imaging Technologies

Update on new imaging technologies in sentinel node detection

2021 ◽  
Vol 72 (6) ◽  
Author(s):  
Silvia Pelligra ◽  
Giuseppe Scaletta ◽  
Stefano Cianci ◽  
Salvatore Gueli Alletti ◽  
Stefano Restaino ◽  
...  
Keyword(s):  
Sentinel Node ◽  
Sentinel Node Detection ◽  
Imaging Technologies

Principles and Imaging Technologies of Magnetic Resonance Phase Imaging

2013 ◽  
Vol 29 (6) ◽  
pp. 407
Author(s):  
Yaxian LOU ◽  
Gang ZHENG ◽  
Li CAO ◽  
Zhiying PAN ◽  
Tiezhu ZHAO ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Phase Imaging ◽  
Imaging Technologies

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

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