Hyperspectral imaging technique for plant leaf identification

Hyperspectral imaging is the procedure to gather and handle information across the electromagnetic spectrum. The fundamental objective of hyperspectral imaging is to achieve the spectrum for every pixel in the picture. The spectrum helps in computer vision, i.e., locating items, material detection or process discovery. This approach is constantly developing in the field of remote sensing applications. The wide spectrum range, offers a high spectral resolution, thus permitting the detection and understanding of the exterior and material components of the viewed image. Numerous elements, for example, defective imaging optics, climatic disturbances, optical brightening impacts and sensor noise cause the corruption of the procured image quality, making spatial determination amongst the costliest and hardest to improve in imaging frameworks. Blended pixels, i.e., pixels containing a blend of diverse materials, are normal in hyperspectral pictures due to such a requirement. In this work, we propose the use of the rich spectral data of hyperspectral images to resolve classification issues.