This project aims to apply deep learning methodologies to solve the issue of locating and controlling pest infestations in cucumber plants. The study begins with a summary of the importance of the cucurbit family and its health advantages, emphasizing the adverse effects of pests such as aphids, red pumpkin beetles, fruit flies, whiteflies, and serpentine leaf miners on cucumber farming. Training YOLO and COCO models on the dataset and assessing their performance using metrics like mean average precision (mAP), precision, and recall constitute the core of the study. The overall findings of the study highlight the significance of deep learning approaches in developing a tool that is accurate and useful for identifying cucumber plant pests, with possible applications to agricultural pest management techniques. It could be observed that on running Roboflow with COCO model a precision of 86% was obtained compared to YOLO model.