Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery
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Abstract (arXiv)
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.
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References · 3
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farmingcited by 22
- 2019Vine disease detection in UAV multispectral images with deep learning segmentation approachcited by 8
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