Pest detection remains a significant challenge in precision agriculture, particularly in accurately identifying and classifying insects, which are pivotal for deploying effective pest management strategies. Optimizing farming practices while ensuring environmental sustainability demands robust Integrated Pest Management (IPM) systems. These systems encompass detection, identification, targeted application of management techniques, and meticulous recordkeeping. The success of IPM hinges on the timely execution of pest detection tasks, emphasizing continuous monitoring and comprehensive data collection. Despite the critical importance of frequent monitoring, the current insect detection and surveillance methodologies are often labor-intensive, time-consuming, and prone to inefficiencies, leading to potential crop damage due to the prioritization of other pressing agricultural tasks. Typically, inspections are conducted several times a week; however, increasing the frequency of daily checks would significantly enhance the accuracy of pest management decisions. Yet, the practical implementation of daily inspections is hindered by challenges related to cost, labor, and feasibility. This paper addresses these challenges by focusing on the complexities of quaternion-based image segmentation for pest detection. The integration of machine vision techniques into mobile embedded systems presents a promising solution to enhance the efficiency and effectiveness of pest management practices in agriculture, ultimately contributing to more sustainable and productive farming outcomes. Looking towards future advancements, the application of Few Shot Learning (FSL) algorithms offers a transformative potential for pest detection. FSL algorithms, designed to learn from a minimal number of annotated examples, can significantly improve the precision and adaptability of pest detection systems. By leveraging FSL, agricultural systems can overcome the limitations of data scarcity, providing accurate pest identification even with limited labeled examples. This capability is precious in dynamic agricultural environments where new pest species and disease symptoms frequently emerge.