Classification of Garlic Land Based on Growth Phase using Convolutional Neural Network

Indonesian Government needs to monitor the realization of garlic land with production plans in several production areas at growth season. A previous study, which used Sentinel-1A satellite imagery and Convolutional Neural Networks to classify garlic land, needed more information on growth phases. The study aims to address that limitation by creating a garlic land classification model based on the growth phase using Convolutional Neural Networks. The dataset comprises 446 preprocessed Sentinel-2 images cross-referenced with drone ground truth data. The model used both VGG16 and VGG19 architectures. Hyperparameter tuning was applied to obtain optimal values. After evaluating three scenarios (VGG16 base model, modified VGG16, and modified VGG19), the best model was obtained from the modified VGG19, which had an accuracy rate of 81.81% and a loss function of 0.71. The study successfully classified garlic land based on growth phase, with a precision rate of 0.43 for initial growth and vegetation classes, and 0.22 for the harvest class. The study offers an alternative to monitoring garlic production throughout growth phases with satellite imagery and deep learning.

@article{Mukhibah2023,
  title     = {Classification of Garlic Land Based on Growth Phase using Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Durrotul Mukhibah and Imas Sukaesih Sitanggang and Annisa},
  doi       = {10.14569/IJACSA.2023.01406100},
  url       = {https://doi.org/10.14569/IJACSA.2023.01406100}
}