Weed Detection In Paddy Fields Using Neural Network

Abstract

In recent years, significant advancements in deep learning, computer vision, and machine learning have the potential to revolutionize agricultural practices by modernizing crop management and yield prediction. A persistent challenge faced by farmers is the presence of invasive weeds, which can severely impact crop growth by competing for essential resources such as water, nutrients, and sunlight. Additionally, accurately predicting crop yield is crucial for farmers to optimize resource allocation, minimize costs, and maximize profits .Recent progress in computer vision offers a cost-effective approach to predicting crop yield using state-of-the-art algorithms. This project aims to address longstanding agricultural challenges by applying novel methodologies. Specifically, we will develop a robust methodology for collecting data on weed detection and establish an image processing pipeline. The collected data will be utilized to train advanced object detection models, such as the CNN Mobile, for accurate weed detection. Data will be leveraged in a CNN and deep learning-based model to distinguish between weeds and crops effectively. By leveraging these cutting-edge technologies, we aim to provide farmers with innovative solutions to enhance crop management and improve agricultural productivity.