Analysis of Adaptive Threshold Value using Weighted Global and Local Approach Method for Herbs Leaves Image Data

The recognition of the health benefits linked to integrating herbs as food constituents has gained substantial attention. Herbs possess the potential to amplify flavours without the adverse health consequences associated with other ingredients. This paper explores the integration of image analysis techniques, particularly edge detection and adaptive thresholding methods, to revolutionize plant identification and research, particularly in the context of herbal leaves. A novel Weighted Global and Local Approach is introduced, aiming to balance the extraction of broad image features with the preservation of intricate details, thereby enhancing segmentation precision. The integration of digital image processing into various domains underscores its importance, with image segmentation as a foundational aspect, aiding in the extraction of relevant information from images. Edge detection techniques, particularly the Canny edge detector, are pivotal in this process due to their capacity to enhance signal-to-noise ratios, resist noise, and remove noise through smoothing methods. The proposed method is intended to dynamically change threshold values by combining global and local features, resulting in accurate segmentation that captures both broad and fine-grained aspects of herbal leaves. Experimental results suggest that the method outperforms existing approaches, highlighting its potential for botanical research, medicinal plant identification, and image analysis applications.