Smart Imaging for Agriculture Data with Road Detection Integration
| dc.contributor.author | PANG CUI FONG | |
| dc.date.accessioned | 2026-04-21T06:54:41Z | |
| dc.date.issued | 2025 | |
| dc.description | This project, "Smart Imaging for Agriculture Data with Road Detection Integration," focuses on enhancing road detection algorithms tailored to the unique challenges of oil palm plantations. The YOLO algorithm, which is implemented in Python using the PyTorch framework, addresses obstacles such as dense vegetation, varying lighting conditions, and uneven terrain that hinder accurate road detection. The objectives include refining the existing algorithm for better adaptability, testing its performance under diverse agricultural conditions, and evaluating it against traditional methods to ensure improvements in accuracy, robustness, and efficiency. The enhanced algorithm is designed to process visual data, identify road networks, and provide actionable insights for effective plantation management. Through rigorous testing and validation using both controlled and real-world datasets, the project aims to deliver a reliable tool for sustainable agricultural management. The results contribute to advancing precision agriculture by enabling informed decision-making and resource optimization in large-scale plantation settings. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/436 | |
| dc.language.iso | English | |
| dc.publisher | UNIVERSITI MALAYSIA SARAWAK | |
| dc.relation.ispartofseries | Faculty of Computer Science and Information Technology | |
| dc.title | Smart Imaging for Agriculture Data with Road Detection Integration | |
| dc.type | Final Year Project |