The thriving agriculture sector in India plays a crucial role in supporting our economy. However, pests pose a significant threat to crop yield, necessitating the implementation of advanced methods for their detection and effective mitigation. Traditional techniques could be more efficient, emphasizing the need for sophisticated solutions. Integrating plant pest detection tools becomes crucial to monitor infestations, ensuring crop quality while reducing reliance on pesticides. This research explores the intersection of IoT, Deep Learning (DL), Computer Vision, and Agriculture to tackle the issue of pest infestations impacting crop production. The study of the proposed Smart Plant Pest Identification System integrates DL algorithms in conjunction with IoT devices to facilitate the early identification of plant pests through advanced image processing techniques. The Smart Plant Pest Identification System incorporates components such as Raspberry Pi and Raspberry Pi Camera Sensor. These components are seamlessly integrated with an underlying Deep Learning (DL) model designed to identify and categorize plant pests in real time. This innovative approach provides farmers with the capability to monitor crop growth, receive instant pest notifications, and implement timely preventive measures. Furthermore, this study includes an extensive comparative analysis of the efficacy of various DL algorithms. The integration of these advanced technologies signifies a significant advancement in precision agriculture, offering the potential for enhanced productivity and sustainable crop yields.