Weed Plant Detection in Agricultural Field Using Deep Learning Integrated with IOT

The Weed Plant Detection in Agricultural Field Images explores the application of Deep Learning (DL) methods in the field of agriculture, specifically focusing on weed detection and classification. These tasks are crucial for weed management. Achieving higher crop yields. Identifying weeds within crops presents challenges due to their appearance. The paper examines data collection, dataset preparation, DL algorithms for weed identification, and evaluation metrics. It highlights the use of learning techniques that involve pre-trained models. The introduction of an approach utilizing the VGG16 Algorithm is discussed, which combines learning with advanced image processing to enable real-time weed identification. This approach aims to minimize yield loss and reduce herbicide usage. It also emphasizes that this technology is scalable and adaptable to crops while integrating into autonomous farming equipment, promoting sustainable agriculture.