This chapter presents the design and development of an artificial intelligence–based autonomous pesticide robot capable of performing targeted spraying in orchard environments. Conventional spraying methods often result in excessive chemical usage, uneven coverage, and operator exposure to hazardous substances. To address these limitations, the proposed system integrates computer vision algorithms and deep learning detection algorithms to detect tree canopies, identify pest-affected zones, and apply pesticides precisely where needed. A deep learning model is employed for real-time foliage and pest-spot recognition. The robot dynamically adjusts spray intensity and angle based on canopy density and target location, significantly reducing chemical waste. Experimental field tests demonstrate that the proposed system achieves high detection accuracy and consistent spray distribution compared to traditional blanket spraying methods. The results highlight the potential of AI-driven robotic systems to improve agricultural sustainability, reduce human risk, and optimize crop protection practices