Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery

Kui Zhao, Siyang Wu, Chang Liu, Yue Wu, Natalia Efremova
11/25/2023
cs.CVeess.IV

No confident code implementation found for this paper. 4 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers

86% match2026

Crop identification using deep learning on LUCAS crop cover photos

85% match2023

Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis

84% match1 repo2482023

SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution

84% match2023

Tackling fluffy clouds: robust field boundary delineation across global agricultural landscapes with Sentinel-1 and Sentinel-2 Time Series

84% match1 repo432024

AI for Agriculture: the Comparison of Semantic Segmentation Methods for Crop Mapping with Sentinel-2 Imagery

83% match2023

Cite this paper

@article{zhao2023precision,
  title  = {Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery},
  author = {Kui Zhao and Siyang Wu and Chang Liu and Yue Wu and Natalia Efremova},
  year   = {2023},
  eprint = {2403.09651},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.09651v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.09651.svg)](https://codeofpaper.com/paper/2403.09651)

Discussion