About this Research Topic
Hyperspectral spectroscopy is an emerging technology in which the advantages of optical spectroscopy, as an analytical tool, are combined with two-dimensional object visualization at a complete spectrum by using hundreds of thousands of narrow bands. The recorded spectra have fine wavelength resolutions and cover a wide range of wavelengths. Every pixel in the image has rich information of continuous spectrum which can be used to identify the objects in the scene with great precision and detail that is not attainable by other imaging technologies. Hyperspectral imaging (HI) can take advantage of structural relationships among spectra allowing elaborate models for accurate classification of chemical and structural compositions of objects. HI has been gaining popularity in agriculture for determining the quality of a wide range of agricultural products, including forages, grains and grain products, oil-seeds, fruits, vegetables, dairy products and meat. HI provides a powerful method to visualize and quantify the composition of agricultural products and medical samples because it meets the criteria of being accurate, reliable, rapid, non-destructive and inexpensive.
Hyperspectral imaging technology has great potential for many difficult classification and diagnostic applications in both agriculture and medical science. Recent years have witnessed new developments regarding HI in the innovative applications of the technology and the new analytical methods for dissecting the spectra images. HI has been reported to be used in conjunction with other new technologies and led to major improvements in diagnostic capability and identification accuracy. For example, hyperspectral imaging has been paired with microscopy, magnetic resonance imaging, and nuclear magnetic resonance in post-harvest and biosecurity screening of invasive pests and determining crop harvest quality. Portable hyperspectral instruments are being experimented on autonomous robots and drones to achieve plant disease detection and crop phenotyping in intelligent and automated farming. The goal of this Research Topic is to present a platform for researchers where novel applications and new development of hyperspectral imaging can be critically assessed and shared, so that more effective detection and classification methods can be further developed.
This Research Topic focuses on new developments in hyperspectral and multispectral imaging, in both the novel applications and the development of new methods for interpreting the newly derived data, particularly in agriculture and medical sciences. The topic welcomes, but is not limited to, Original Research and Reviews related to the hyperspectral imaging technology in agriculture and biosecurity areas:
• Agricultural product quality determination
• Plant disease detection
• Pests and pathogen diagnostics
• Chemical residue evaluation
• Drone and sensor-based phenotyping
• Seed identification and classification
Important Note: All submissions/contributions to this Research Topic must be in line with the scope of the journal/section they are submitted to. While authors are encouraged to draw from other disciplines to enrich their papers where relevant, they must ensure papers fall within the scope of the journal/section, as expressed in its mission statement.
Prof. Zhou holds patents on the management of agricultural pests. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Hyperspectral imaging technology has great potential for many difficult classification and diagnostic applications in both agriculture and medical science. Recent years have witnessed new developments regarding HI in the innovative applications of the technology and the new analytical methods for dissecting the spectra images. HI has been reported to be used in conjunction with other new technologies and led to major improvements in diagnostic capability and identification accuracy. For example, hyperspectral imaging has been paired with microscopy, magnetic resonance imaging, and nuclear magnetic resonance in post-harvest and biosecurity screening of invasive pests and determining crop harvest quality. Portable hyperspectral instruments are being experimented on autonomous robots and drones to achieve plant disease detection and crop phenotyping in intelligent and automated farming. The goal of this Research Topic is to present a platform for researchers where novel applications and new development of hyperspectral imaging can be critically assessed and shared, so that more effective detection and classification methods can be further developed.
This Research Topic focuses on new developments in hyperspectral and multispectral imaging, in both the novel applications and the development of new methods for interpreting the newly derived data, particularly in agriculture and medical sciences. The topic welcomes, but is not limited to, Original Research and Reviews related to the hyperspectral imaging technology in agriculture and biosecurity areas:
• Agricultural product quality determination
• Plant disease detection
• Pests and pathogen diagnostics
• Chemical residue evaluation
• Drone and sensor-based phenotyping
• Seed identification and classification
Important Note: All submissions/contributions to this Research Topic must be in line with the scope of the journal/section they are submitted to. While authors are encouraged to draw from other disciplines to enrich their papers where relevant, they must ensure papers fall within the scope of the journal/section, as expressed in its mission statement.
Prof. Zhou holds patents on the management of agricultural pests. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: Hyperspectral Imaging, Biosecurity, Diagnostics, Post-Harvest, Agricultural Applications
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
