Inspection of Crop-Weed Image Database Using Kapur's Entropy and Spider Monkey Optimization

Image assessment measures are commonly employed in different domains to extract the helpful information to take essential decisions. This paper implements a soft-computing approach to examine the Benchmark Crop-Weed (BCW) images of Computer Vision Problems in Plant Phenotyping (CVPPP2014) challenge database. The proposed work executes a hybrid procedure based on Spider Monkey Optimization (SMO) algorithm and Kapur’s multi-thresholding and the Watershed Segmentation (WS) based extraction. After extracting the Crop-Weed regions of BCW pictures, the superiority of the proposed tool is then assessed by implementing a relative study among extracted segment and its related ground-truth. Additionally, the prominence of SMO is validated against the Bat-Algorithm (BA) and Firefly-Algorithm (FA). The outcome of this study authenticates that SMO-based technique is competent in examining the BCW pictures with significant accuracy and precision.

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