Low-cost hyper-spectral imaging system using a linear variable bandpass filter for agritech applications.

Area of Science:

• Agricultural Science
• Optics and Photonics
• Computer Vision

Background:

• Hyperspectral imaging (HSI) enables non-destructive, large-area crop monitoring.
• Current HSI systems are often bulky and costly, limiting agricultural adoption.
• There is a need for affordable, compact HSI solutions for precision agriculture.

Purpose of the Study:

• To develop a low-cost, prototype hyperspectral imaging camera for agricultural applications.
• To evaluate the spectral performance and classification accuracy of the developed system.
• To demonstrate the system's utility in assessing potato plant health and identifying plant species.

Main Methods:

• A linear variable filter (LVF) was designed and integrated into a prototype HSI camera.
• The camera operated with spectral performance in the 450–900 nm range.
• A feature extraction and classification algorithm was developed and applied to image data.

Main Results:

• The prototype HSI camera demonstrated good spectral performance between 450 and 900 nm.
• The system achieved approximately 88% accuracy in determining potato plant health.
• The algorithm successfully differentiated between rocket, lettuce, and spinach species.

Conclusions:

• The developed linear variable filter-based hyperspectral imaging system shows promise as an entry-level, cost-effective solution for agriculture.
• The system's capability in non-destructive crop monitoring, health assessment, and species identification is validated.
• This technology has the potential to make advanced crop analysis more accessible to farmers.