Extraction of Cover Density Map of Rice Seedlings
Published in Undergraduate Research Project, 2023
This research focused on developing an automated detection system for rice seedlings in UAV images of paddy fields using advanced computer vision techniques and transfer learning.
Project Overview
Objective: Detect and map rice seedlings in aerial imagery for precision agriculture applications
Methodology:
- Applied transfer learning using Faster R-CNN architecture
- Processed UAV imagery of paddy fields
- Developed cover density mapping algorithms
- Optimized for agricultural monitoring and yield prediction
Key Contributions:
- Automated rice seedling detection in complex agricultural environments
- Application of state-of-the-art object detection to precision agriculture
- Potential for crop monitoring and yield estimation
- Scalable approach for large-scale agricultural assessment
Applications:
- Precision agriculture and crop monitoring
- Yield prediction and optimization
- Resource allocation for farming operations
- Early detection of crop health issues
Supervisor: Dr. Ehsan Pazouki
Institution: Computer Vision Research Group, Shahid Rajaee Teacher Training University
Duration: Fall 2022 - Spring 2023
Recommended citation: Gholizadeh, M.S. (2023). "Extraction of Cover Density Map of Rice Seedlings." Shahid Rajaee Teacher Training University.
Download Paper