The Role of Computer Vision in Automated Weed Detection and Management

Weed infestation is a significant challenge in modern agriculture, leading to reduced crop yields and increased reliance on herbicides. Traditional weed management methods are often labor- intensive, environmentally harmful, and inefficient. This paper explores the role of computer vision in automated weed detection and management, highlighting its potential to enhance precision agriculture. By leveraging deep learning models such as Convolutional Neural Networks (CNNs) and image processing techniques, computer vision enables accurate identification and classification of weeds in real-time. Integration with autonomous robotic systems and drones allows for targeted herbicide application, reducing chemical usage and promoting sustainable farming practices. Furthermore, advancements in hyperspectral and multispectral imaging improve weed differentiation from crops, enhancing detection accuracy. Despite challenges such as dataset variability, environmental factors, and model generalization, computer vision offers a promising solution to revolutionize weed management. This study emphasizes the need for further research to optimize AI-driven weed control systems and ensure their widespread adoption in precision agriculture.