Hyper spectral image classifications for monitoring harvests in agriculture using fly optimization algorithm. (October 2022)

• Record Type: Journal Article
• Authors: S, Shitharth; Manoharan, Hariprasath; Alshareef, Abdulrhman M.; Yafoz, Ayman; Alkhiri, Hassan; Mirza, Olfat M.
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Remote sensing -- Hyperspectral images -- Bio-inspired algorithm -- Agriculture; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108400
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Many advanced technologies related to agricultural applications are not being used by crofters due to various factors as every designed equipment is produced for a single usage mechanism. On the other hand, the implementation of remote sensing techniques using hyperspectral images is developing for providing more useful information at a reduced cost. The hyperspectral image processing mechanism is used for managing and sensing the growth of crops from remote locations. 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. Graphical abstract: Image, graphical abstract