International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Mani golakoti 6 2 (March - April) 2024 04 March-April enCNNDeep LearningImage ProcessingData Collection MethodologyObject Detection. 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 costeffective approach to predicting crop yield using stateoftheart 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 learningbased model to distinguish between weeds and crops effectively By leveraging these cuttingedge technologies we aim to provide farmers with innovative solutions to enhance crop management and improve agricultural productivity Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2024 All Rights Reserved