Artificial Intelligence (AI) constitutes one of the most recent major advancements in technology, transforming research, teaching, and traditional practices across various fields. One area where AI is already making a significant impact and is projected to continue growing is advanced precision agriculture. This research employs an AI-assisted workflow to accelerate the implementation of algorithms that utilize convolutional neural networks (CNN) and real-time object detection models to accurately differentiate between useful crops and harmful weeds. The study is divided into two phases. The first phase, covered in this paper, focuses on creating a virtual crop environment that serves as the foundation for training and validating the model. The second phase, planned for future work, aims to integrate the trained model into a drone system for autonomous, targeted spraying, enhancing the efficiency and sustainability of agricultural practices.