Paper Contents
Abstract
The study delves into the application of IoT (Internet of Things) technology coupled with image processing and Convolutional Neural Networks (CNNs) in the domain of smart agriculture, specifically for weed detection. As agriculture faces challenges of increasing productivity while minimizing resource use and environmental impact, innovative solutions like IoT-based weed detection offer promising prospects.This research explores the utilization of IoT devices equipped with cameras to capture images of agricultural fields. These images are then processed using image processing techniques to identify and isolate weeds. Subsequently, CNNs, a class of deep learning algorithms known for their efficacy in image recognition tasks, are employed to classify the detected objects as weeds or non-weeds.The study highlights the significance of this study in addressing the pressing need for sustainable agricultural practices. By automating weed detection, farmers can optimize herbicide usage, reduce labor costs, and enhance crop yields. Furthermore, the abstract underscores the potential of IoT-enabled solutions to revolutionize various aspects of agriculture, paving the way for smarter and more efficient farming practices.
Copyright
Copyright © 2024 Akash MAthur. This is an open access article distributed under the Creative Commons Attribution License.
