Hyperspectral imaging is a detection technology that integrates imaging technology and spectral analysis technology. Unlike traditional imaging which only captures spatial information (forming a "picture"), it can simultaneously collect continuous and fine spectral information of each pixel in the imaging area.

Simply put, it is equivalent to taking a "spectral fingerprint" for every point in the scene while taking a photo. For example, when observing a leaf, a common camera can only show its green appearance, but hyperspectral imaging can obtain the spectral data of the leaf in hundreds of narrow wavelength bands (from visible light to near-infrared, mid-infrared, etc.), and further analyze the leaf's internal structure, water content, and nutrient status through these data.

Its core feature is the high spectral resolution—the wavelength interval between adjacent spectral bands can be as small as a few nanometers (nm), which enables it to distinguish subtle differences in material composition that are invisible to the naked eye or ordinary cameras.

Its typical application scenarios include:

Agricultural field: Detecting crop growth status (such as nutrient deficiency, water content), identifying crop varieties, and assessing yield and quality (e.g., judging sugar content and maturity of fruits like apples and grapes).

1. Food safety testing:

Identifying adulteration of food (e.g., distinguishing fake honey, detecting mixed impurities in grain), and detecting pesticide residues or harmful substances in food.

2. Environmental monitoring:

Monitoring water quality (e.g., analyzing content of pollutants, algae, and dissolved organic matter in water), and identifying types and coverage of vegetation in ecological zones.

3. Medical and biomedical fields:

Assisting in early diagnosis of diseases (e.g., detecting subtle changes in skin lesions, analyzing tissue composition), and researching drug components and their distribution in organisms.

4. Industrial non-destructive testing:

Inspecting defects of industrial products (e.g., detecting internal cracks of metal parts, identifying surface flaws of composite materials), and monitoring product quality in production lines (e.g., judging uniformity of coating thickness).