The agriculture industry has several challenges, including pesticide misuse, environmental impacts, and increased costs of production. Older technologies for pesticide application and management have had high inefficiencies resulting in overdosing pesticide products and contamination and degradation of soils and water resources (Ferrag et al. IEEE Access, 8:32031-32053, 2020). This research describes an AI-based automated pesticide sprayer developed using engineering green sensors to avoid the issues. At the most, the system received deep learning input from the ESP32 camera, interfaced with IoT, to assess crop populations in real-time and pesticide application. The AI model was able to determine the pest-infested portions of the plants accurately and applied pesticides to those areas sparingly to minimize chemical usage and damage to the environment. The system also integrated cybersecurity elements, such as encrypted data transmission, secure authentication, and anomaly checking, to protect sensitive data in the agricultural sector and frustrate unauthorized users, as well as hacking attempts. These security measures ensure the integrity and trustworthiness of the automated AI-based decision-making strategy. The field application resulted in 95% accuracy in pest detection with limited pesticide usage, but improved crop health. The system optimizes resources, reduces costs of operation, and enables organic farming throughout.