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About

Akanksha Rastogi is a researcher specializing in computer vision, machine learning, and agricultural robotics, with a particular focus on the automation of dairy farming systems. Her work centers on the development of intelligent vision systems for next-generation automatic milking robots, addressing one of the most technically demanding challenges in precision livestock farming: accurate and efficient teat detection. Rastogi's most significant contribution is her comparative analysis of teat detection algorithms, pitting the deep learning-based YOLO framework against traditional Haar-cascade methods, which has garnered 21 citations and established itself as a key reference in agricultural robotics literature. Alongside this, her earlier foundational work explored multi-modal imaging approaches — integrating Time-of-Flight, RGB-D, and thermal imaging technologies — to create robust conceptual frameworks for robotic milking systems. Her research trajectory demonstrates a consistent commitment to bridging machine learning innovation with real-world agricultural applications. By developing vision systems capable of giving robotic manipulators faster and more precise detection capabilities, Rastogi's work contributes meaningfully to improving efficiency, animal welfare, and labor automation in modern dairy operations. Her growing citation record reflects the relevance and timeliness of her contributions to this emerging field.