Paper Title
AGROGUARD: AN AI-POWERED WEED DETECTION
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Registration ID: IJNRD_323309
Published ID: IJNRD2604280
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Keywords
YOLO11, weed detection, precision agriculture, deep learning, object detection, smart farming, real-time detection
Abstract
Weed infestation is a problem for farmers all over the world. It greatly reduces crop yield and overall productivity. Old ways of controlling weeds like removing them by hand and spraying chemicals everywhere take a lot of time are expensive and harm the environment. To solve these problems we created AgroGuard. It is a system that helps detect weeds. This system supports precise farming practices. AgroGuard uses the YOLO11 model to identify weeds from pictures of fields. This model is very good at finding objects in time. The system can recognize twelve types of weeds. It was trained on a varied set of weed pictures. We used techniques to make sure it works well in various field conditions, lighting and picture angles. Users can upload pictures of their fields. The system then accurately shows where the weeds are. Experimental results show that AgroGuard works with accuracy and reliability. It greatly reduces the time and effort needed to identify weeds. By allowing targeted weed management AgroGuard helps reduce chemical use. This contributes to friendly farming. This approach follows the principles of precision agriculture. Inputs are only applied where necessary. This leads to efficiency, reduced labor and optimized resource use. Overall AgroGuard is an effective solution for modern weed management challenges. It helps farmers deal with weed infestation in a way. AgroGuard makes farming more efficient and environmentally sustainable.
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How To Cite (APA)
Pagidimarry Suhas, Anthamgari Pramodhar, Pilli Shiva Kumar, & Ricabi Ujhwaal Sagar (April-2026). AGROGUARD: AN AI-POWERED WEED DETECTION . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), c666-c671. https://ijnrd.org/papers/IJNRD2604280.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : c666-c671
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Paper Reg. ID: IJNRD_323309
Published Paper Id: IJNRD2604280
Research Area: Other area not in list
Author Type: Indian Author
Country: Hyderabad, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604280.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604280
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