India, with three-fourths of its population dependent on agriculture, is plagued by severe crop loss due to pest infestation, particularly in staple crops like rice, wheat, maize, and soybeans. This paper proposes an embedded system of real-time pest detection and precise pesticide spraying to enhance productivity. The system employs deep learning with a Residual Neural Network (ResNet) and Quadra-attention, residual, and dense fusion techniques for enhanced pest image classification. High-resolution images of crop leaves are captured, pre-processed, and analyzed for pest detection. Upon detection, the system selects the appropriate pesticide and activates an autonomous robotic sprayer. Driven by an Arduino NANO-based module with an L293D motor driver, the robotic system automatically navigates through fields, ensuring precise pesticide application without waste and infrastructure costs. With IoT integration and 99.80% validating accuracy, this system optimizes pesticide use, enhances crop health, and enhances yield, offering a cost-effective automated pest management system for sustainable agriculture.