@inproceedings{SisLab4682,
       booktitle = {2021 6th International Conference on Engineering Mechanics and Automation},
           month = {November},
           title = {Design of UAV system and workflow for weed image segmentation by using deep learning in Precision Agriculture},
          author = {Duc Anh Dao and Truong Son Nguyen and Cong Hoang Quach and Duc Thang Nguyen and Minh Trien Pham},
            year = {2021},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4682/},
        abstract = {Collecting and analyzing weed data is crucial, but it is a real challenge to cover a large area of fields or farms while minimizing the loss of plant and weed information. In this regard, Unmanned Aerial Vehicles (UAVs) provide excellent survey capabilities to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. This paper addresses the practical problem of the weed segmentation task using a multispectral camera mounted on a UAV. We propose the method to find the ideal workflow and system parameters for UAVs to maximize field crop coverage while providing data for reliable and accurate weed segmentation. Around the segmentation task, we examine several Convolutional Neural Networks (CNNs) architectures with different states (fine-tune) to find the most effective one. Besides that, our experiment using Near-infrared (NIR) and Normalized Difference Vegetation Index (NDVI) -the foremost spectroscopies - as an indicator of the vegetation density, health, and greenness. We implemented and evaluated our system on two farms, sugar beet and papaya, to conclude based on each stage of crop growth.}
}