Design of Autonomous Weed Elimination using Maching Learning Techniques

The abstract presents a system that employs deep learning techniques to automatically detect weeds in agricultural fields. While the system’s efficiency in identifying the presence of weeds is highlighted, the challenges faced by such systems are not explicitly mentioned. Recent techniques such as convolutional neural networks (CNN) are used in this system to teach the model using a dataset of images of weeds and crops. However, the challenges faced by such systems, such as the need for large annotated datasets and the potential for misclassifying weeds, are not addressed. The proposed objective of this study is to demonstrate the effectiveness of using deep learning methods to improve weed management techniques in agriculture. By accurately identifying weeds, farmers can use fewer herbicides and reduce the negative environmental impact of weed control. Overall, this study highlights the potential of deep learning methods in enhancing agricultural practices and reducing the negative environmental effects of weed control.