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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.
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G. Mani Completed B.Tech from JNTU-H in 2007 and M.Tech in JNTU-K in 2014. Presently pursuing Ph.D from GITAM University in the domain of machine learning. She is currently working as assistant Professor in Department Of IT, VIIT, Duvvada, Visakhapatnam. She has 3years of industry experience and 12 years of academia experience. She is a life time member of ACM. Her main research focuses on Data mining, Machine learning, Deep Learning.""
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*Corresponding Author: Mani golakoti, mani.golakoti22@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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