| dc.description.abstract | As the number of farmers willing to engage in agriculture decreases annually, smart agriculture has become an important current development. Currently, most farmers are of middle to advanced age, and as they grow older, their physical abilities may decline, potentially reducing productivity and leading to a shortage of labor in agriculture. Furthermore, the escalating global warming issue in recent years has worsened external environmental conditions, making prolonged exposure to high temperatures unbearable for humans.To address these agricultural challenges, integrating edge devices with object detection technology allows farmers to monitor conditions without prolonged exposure to extreme heat. With the rise of internet technology, IoT (Internet of Things) capabilities have advanced accordingly, offering solutions to agricultural issues that can reduce labor costs.This paper proposes a system that utilizes edge devices combined with object detection technology to identify pests. Specifically, it employs a Raspberry Pi as an edge device and integrates YOLOv5 object detection to achieve pest identification. Detected images are captured and notified to users via Line notify for further action, while detection results are also backed up in an AWS cloud storage bucket. Training of the edge-side detection model is conducted locally, and the trained model is uploaded to AWS cloud to establish the connection between local and edge devices using AWS cloud technologies. This approach significantly reduces data transmission latency, lowers costs, alleviates central processing burdens, and enhances privacy and security. | en_US |