A Deep Learning-based Approach for Vision-based Weeds Detection

Weed detection is an essential component of smart agriculture, and the use of remote sensing technologies has the potential to significantly improve weed management practices, reduce herbicide usage, and increase crop yields. This study proposed an approach to weed detection using computer vision and deep learning technologies. By utilizing remote sensing methods based on DL, this approach has the potential to optimize weed management strategies, minimize herbicide use, and enhance crop productivity. The weed detection algorithm is based on the Yolov8 framework, and a custom model is trained using images from popular datasets as well as the internet. To evaluate the model's effectiveness, it is tested on both validation and testing sets. Furthermore, the model's performance is assessed using images that are not included in the original dataset. As experimental results shown, the deep learning-based approach is a promising solution for weed detection in agriculture.

@article{Wang2023,
  title     = {A Deep Learning-based Approach for Vision-based Weeds Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yan Wang},
  doi       = {10.14569/IJACSA.2023.0141207},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141207}
}