Pests, pose a pervasive threat to agriculture on a national scale. Their voracious feeding on plant roots results in diminished crop yields, leading to economic losses for farmers and potential food security challenges. The integration of advanced sensor technologies and deep learning offers a promising avenue to address the impact of Pests, enabling timely and accurate detection. This, in turn, allows for targeted and efficient pest management strategies, mitigating the widespread repercussions of infestations and fostering sustainable agricultural practices on a national level. This paper explores the transformative synergy between sensor technologies and deep learning techniques for the identification and density detection of pests in agriculture. Traditional methods face limitations, prompting a shift towards advanced technologies. We survey the landscape of sensor technologies, including image sensors, acoustic sensors, and soil sensors, highlighting their real-time, high-dimensional data contribution. Integration with deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, offers a precise and adaptive approach to pest management. The potential impact of this integration is substantial, promising increased crop yields, reduced reliance on broad-spectrum pesticides, and improved environmental sustainability. The review underscores the importance of continuous adaptation and scalability, setting the stage for a future where technology plays a pivotal role in ensuring the health and productivity of agricultural landscapes.