An improved segmentation technique for multilevel thresholding of crop image using cuckoo search algorithm based on recursive minimum cross entropy

Abstract

Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (CS) using Levy flight is used to find the optimal threshold for this objective function. Because real-time applications require less processing time while maintaining high performance, which is validated by the CS algorithm using recursive minimum cross entropy (R-MCE-CS) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind-driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

MCMC algorithm based on Markov random field in image segmentation

“…The accuracy of this method was tested on 10 crop images. The experimental outcomes denoted that the proposed method provided the most promising results and improved accuracy [ 9 ]. Yan X and Weng proposed an optimized fast image segmentation model based on local pre-fitting images.…”

“…In Eq (9), z x and z y represent the neighboring grayscale value vectors of pixels x and y, respectively. kz x À z y k 2 2s is the Gaussian weighted Euclidean distance.…”