This book presents an innovative approach to modern agriculture through the development of an artificial intelligence-based weed detection system. It explores how advanced computer vision and machine learning techniques can be applied to automatically identify and classify weeds with high precision in agricultural fields. By analysing high-resolution images captured via cameras and drones, the system extracts critical features such as leaf shape, colour, texture, and growth patterns to effectively distinguish weeds from crops. A key highlight of the work is the comparative analysis of multiple machine learning models, demonstrating the superior performance of an ensemble approach that integrates Convolutional Neural Networks (CNNs) with Random Forest classifiers. This hybrid model significantly improves detection accuracy and reliability compared to standalone techniques. The proposed system supports precision agriculture by enabling targeted pesticide application, thereby reducing chemical usage, lowering environmental impact, and improving crop yield. This book is a valuable resource for researchers, academicians, and practitioners interested in artificial intelligence, image processing, and sustainable agricultural technologies.