Comparison of Resnet Models in UNet Classifier for Mapping Oil Palm Plantation Area with Semantic Segmentation Approach

In 2023, Indonesia experienced an increase Industrial oil palm plantations grew by 116,000 hectares in 2023, an increase of 54% from the previous year. Oil palm is one of the main agricultural commodities in Indonesia, with a significant contribution to the national economy. However, manually mapping and monitoring oil palm land is still a big challenge. This manual process is labor-intensive, time-consuming and costly. In addition, the accuracy of the data generated is often inadequate, especially in identifying the actual crop condition and land area. Remote sensing (RS) provides extensive and comprehensive data on oil palm land and crop conditions through satellite and drone imagery. In this research, a method of mapping oil palm plantations is proposed using medium resolution sentinel satellite imagery data that is widely available and has adequate spatial resolution. In addition, it is proposed to implement the artificial intelligence (AI) method with deep learning (DL) using the UNet classifier which has been proven in previous studies to provide sufficient accuracy. The research will develop a DL model/architecture with ResNet-34 and ResNet-50 backbones that are expected to further improve the accuracy of segmentation results so that it can be used in oil palm land mapping. The research concluded that semantic segmentation using the UNet classifier with ResNet-34 and ResNet-50 backbone produced F1 scores of 0.89 and 0.922, respectively. The accuracy obtained at the inference/deployment model stage for each ResNet-34, and ResNet-50 backbone was 88.8% with an inference duration of 10 minutes and 91.8% with an inference duration of 20 minutes.

@article{Panghurian2024,
  title     = {Comparison of Resnet Models in UNet Classifier for Mapping Oil Palm Plantation Area with Semantic Segmentation Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Fepri Putra Panghurian and Hady Pranoto and Edy Irwansyah and Fabian Surya Pramudya},
  doi       = {10.14569/IJACSA.2024.0150741},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150741}
}