Research Projects

Throughout history, science has driven many of the world's most significant and impactful revolutions. As we stand on the brink of another shift fuelled by technological advancements like artificial intelligence (AI), India must take proactive steps to lead this change. Since agriculture is the backbone of our economy—contributing approximately 19% to the GDP, with the livestock sector alone accounting for 30.47% (₹6,54,937 crores) of agricultural GDP—this area must be our primary focus to enhance the lives of millions of farmers and consumers. This is particularly important because the application of technology in livestock, compared to other sectors, is still in its infancy despite supporting 20.5 million people. In India, the lack of technological resources for small ruminant farms limits their potential for growth and sustainability. This study aims to fill the gap by designing and validating sensor and computer vision-driven automated systems for livestock monitoring, ensuring accurate, reliable, and robust data collection. Using this data we aim to develop AI-driven models focused on unique identification, ethological and morphometric predictions, and welfare assessment in livestock. Models shall be created using computer vision as well as sensor data and multiple algorithms shall be deployed and tested including Convolutional Neural Networks, Decision Trees and Transfer Learning. These models shall then be deployed and integrated with the previously developed Decision Support System: Smart Sheep Breeder (SSB) by the PI, thereby enhancing its capabilities for effective and intelligent livestock management. The PI has done pioneering work in AI in animals for animal breeding, predictions and farm management, leveraging ML algorithms, computer vision and IoT. This research is proposed to build on the previous AI in animal research. This study aims to enhance the progress of the development of animal-centric technologies of which there is a pressing need in India leading to the development of more robust, inexpensive and durable systems. This approach will eliminate drudgery, increase efficiency, enhance genetic progress, and boost farmers' income. It will also promote entrepreneurship—high-tech, high-profit, intensive farms would attach tech-savvy youth to sheep breeding. By aligning farms with developed countries, we can ensure genetically superior animals, and quality livestock products, fostering self-reliance, self-sufficiency, surplus production, and an export-based economy. Progress is needed in developing sensor and image-based systems to reduce drudgery and improve efficiency. Creating species-specific models, considering unique behavioural and physical traits, and addressing the scarcity of region-specific data will be key outcomes, providing valuable resources for future research to tackle challenges in Indian farming systems.