Precision Agriculture: Crop Mapping using Machine Learning and
  Sentinel-2 Satellite Imagery
Kui Zhao Siyang Wu Chang Liu Yue Wu Natalia Efremova
Food security has grown in significance due to the changing climate and its
warming effects. To support the rising demand for agricultural products and to
minimize the negative impact of climate change and mass cultivation, precision
agriculture has become increasingly important for crop cultivation. This study
employs deep learning and pixel-based machine learning methods to accurately
segment lavender fields for precision agriculture, utilizing various spectral
band combinations extracted from Sentinel-2 satellite imagery. Our fine-tuned
final model, a U-Net architecture, can achieve a Dice coefficient of 0.8324.
Additionally, our investigation highlights the unexpected efficacy of the
pixel-based method and the RGB spectral band combination in this task.