Enhancing Precision and Stability: A Cognitive Approach for Pesticide Image Segmentation in Crop Leaves

Abstract

The precise delineation of pests from crop foliage is a crucial stage in the field of intelligent pest identification. This study presents a novel cognitive segmentation methodology that aims to enhance the accuracy and reliability of this crucial procedure. The technique comprises a number of essential stages: To begin with, the proposed method utilises sophisticated image block processing techniques to partition the pest image into smaller, more manageable parts. Additionally, an adaptive learning technique is utilised to carefully choose the initial cluster centres, hence guaranteeing the precision of the segmentation procedure. Subsequently, the use of K-means clustering facilitates the acquisition of initial segmentation outcomes, hence augmenting the procedure of identification. In order to mitigate the impact of leaf veins, the proposed approach utilises three digital morphological characteristics that are closely linked to ellipses. The study involved conducting experimental segmentation trials on crop photos that contained whiteflies. The findings of the study provide compelling evidence that the suggested cognitive segmentation method outperforms existing techniques in terms of accuracy and robustness. This technological development provides a robust basis for future advancements in pest identification and crop management