| Title: | OS2-1 Efficient Weed Detection in Agricultural Landscapes using DeepLabV3+ and MobileNetV3 |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 60-66 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS2-1 |
| Author(s): | Renuka Devi Rajagopal, Manthena Rishit Varma, Heshalini Rajagopal |
| Publication Date: | February 13, 2025 |
| Keywords: | Deep Learning, Semantic Segmentation, Agriculture, Weed Detection, Image Processing, Computer Vision |
| Abstract: | Weed detection is a crucial task in precision agriculture, significantly impacting crop yields and reducing the dependency on herbicides. Effective weed management enhances agricultural productivity by ensuring that crops receive adequate nutrients, water, and sunlight, which weeds would otherwise consume. Traditional weed control methods are labor-intensive and often rely heavily on chemical herbicides, which can have detrimental environmental effects. This paper presents a deep learning approach for weed detection, utilizing the DeepLabv3+ model with a MobileNetv3 backbone. This study underscores the potential of integrating advanced deep learning techniques into agricultural practices, paving the way for more sustainable and efficient weed management strategies. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS2/OS2-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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