The exponential growth of population has resulted in food safety becoming a major concern in global context. To provide food for people and livestock worldwide, it is crucial to implement intelligent solutions that cater to the specific needs of crop cultivation, while maintaining soil quality. Maize holds higher potential than other major crops as it is widely used as industrial raw material, bio-ethanol production, feed and fodder for cattle, besides its primary use as food. Weed management plays a crucial role in maize agricultural practices as it helps ensure optimal crop growth and yield. Conventional weed control methods have limitations that hinder their effectiveness for future weed management. Also, Weed management has become increasingly challenging due to the over-reliance on herbicides that has accelerated the evolution of herbicide-resistant weeds among increasing concerns about effect of pesticides on environment and human health. As a result, there is a growing need for an integrated approach that combines different strategies and utilizes new technologies towards precise and efficient weed management. The work in the following paper utilizes the YOLOv5 object detection algorithm to detect and classify weeds in images. The trained model can then be used for inference on new images to identify and classify weeds.