Hyper spectral image classifications for monitoring harvests in agriculture using fly optimization algorithm

Abstract

Many cutting-edge technologies with regard to agricultural applications are not being employed by farmers for a number of reasons, including the fact that each piece of designed equipment is manufactured for a specific utilisation mechanism. In contrast, the use of hyperspectral remote sensing techniques is expanding to deliver more valuable data at a cheaper cost. The hyperspectral images are created to operate in different locations using different band topologies, making the proposed model more practical and effective with the existing spectrum elements. Since flies movements are used to acquire hyperspectral images with straight-line perception, the proposed method also makes use of the bio-inspired Fly Optimization Algorithm (FOA). The functional efficacy, loss prevention, and error prevention of the FOA, which averages 83 percent, show that it is far better than current practises.

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.