Abstract
Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.
Abstract (translated)
高效利用耕种区域是农业可持续发展和保障粮食安全的一个重要因素。随着发达国家卫星技术的迅速发展，人们正在寻找准确且操作性强的方式来识别耕地。在这种背景下，基于从卫星图像获取的数据进行光谱分析以确定耕地边界的方法被认为是现代农业中最为优化和精确的方法之一。本文提出了一种新方法，利用通过“Google Earth Engine”（GEE）平台获得的卫星数据来确定耕种区域的适宜性和绿色指数。该方法结合了两个强大的算法：“SNIC（Simple Non-Iterative Clustering）超像素”算法以及“Canny边缘检测法”。 SNIC算法将卫星图像中的像素组合成具有类似特性的较大区域（即超像素），从而提供更好的图像分析能力。而Canny边缘检测法则用于识别图像中急剧变化的边界，以确定农业用地的确切边界。这项研究使用了来自Sentinel-2卫星的高分辨率多光谱数据以及Google Earth Engine JavaScript API，并表明所提出的方法在准确且可靠地分类随机选择的农田方面非常有效。 通过结合这两种工具的使用，可以更精确地确定农业土地的边界，减少卫星图像中异常值的影响。基于获得的数据，可以在大面积范围内创建更加准确和可靠的农业监测与资源管理地图。这扩展了云计算平台及人工智能方法在农业领域的应用能力。
URL
https://arxiv.org/abs/2502.04529
