India’s agricultural sector is undergoing a critical transformation to meet the dual challenges of ensuring food security for a growing population and conserving rapidly declining biodiversity. Traditional methods of insect biodiversity monitoring, often manual, labour-intensive, and limited in scale, are proving inadequate in the face of emerging threats such as climate change, pollinator decline, and pest outbreaks. This chapter presents a case study of the Biodiversity Sensor, a cutting-edge solution developed by the Indian Institute of Technology (IIT) Ropar. The Biodiversity Sensor is a solar-powered, AI-enabled, and field-deployable device capable of autonomously detecting, classifying, and quantifying insect species with over 90% accuracy. Built using YOLOv5 deep learning architecture, motion-sensing cameras, and IoT connectivity, the sensor facilitates real-time data collection and cloud-based analytics. Initially focused on monitoring bee pollinators, it now supports broader applications in precision pest management, pollinator conservation, and biodiversity indexing across diverse farm ecosystems. The device has been successfully deployed in over 10 countries, including India, the USA, Germany, and Australia, demonstrating its scalability and adaptability to different agricultural contexts. This chapter explores the sensor’s technological design, training methodology, deployment strategy, and socio-economic impact. It highlights how digital entomology is revolutionising smart agriculture by turning silent ecosystems into data-rich landscapes. By empowering farmers, researchers, and policymakers with actionable biodiversity insights, the Biodiversity Sensor stands as a scalable model for data-driven, climate-resilient, and sustainable agriculture, contributing meaningfully to India’s vision for precision farming and ecological stewardship.