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Deep Learning-Based Weed Mapping Using UAV Images

This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction ‘Deep Learning-Based Weed Mapping Using UAV Images’. The project connects agricultural engineering with artificial intelligence, machine learning, deep learning,…

Drone-Based Agriculture
Project Overview This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction 'Deep Learning-Based Weed Mapping Using UAV Images'. The project connects agricultural engineering with artificial intelligence, machine learning, deep learning, IoT, computer vision, drone analytics, or RAG-style decision support. Students can use the linked 2023-onward paper/source as the academic base and convert it into an implementation-focused final-year project with sensors, datasets, dashboards, mobile/web interfaces, prediction models, or prototype automation.
Research Paper Title Deep Learning-Based Weed Mapping Using UAV Images
Research Paper / PDF Link Open Paper / PDF
Year 2024
Project Area Drone-Based Agriculture
Project Type Weed Mapping
Required Tools / Software Python, OpenCV, YOLO/Deep Learning, UAV/drone imagery, QGIS optional, Streamlit
Main Features / Working Principle Identify weed patches in aerial/drone images using deep learning
Expected Output A weed map for precision spraying planning
Possible Add-ons Add zone-wise pesticide recommendation
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This B.Tech agricultural engineering project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.

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