Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Turin, Italy.
Sensors (Basel). 2020 Apr 29;20(9):2530. doi: 10.3390/s20092530.
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite's output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.
精准农业被认为是在进行特定地点管理实践时追求低投入、高效率和可持续农业的一种基本方法。为了实现这一目标，需要对当地作物的状况进行可靠和最新的描述。遥感，特别是基于卫星的图像，已被证明是作物制图、监测和疾病评估的一种有价值的工具。然而，免费提供的低分辨率或中分辨率卫星图像在某些特定农业应用中存在一些局限性，例如，当作物成行种植时。实际上，在这种情况下，卫星的输出可能会受到行间覆盖的影响，从而提供关于作物状况的不准确信息。本文提出了一种基于深度学习技术的新型卫星图像细化框架，该技术利用从无人驾驶飞行器 (UAV) 机载多光谱传感器获取的高分辨率图像中适当提取的信息。为了训练卷积神经网络，只需要一个单一的 UAV 驱动数据集，这使得所提出的方法简单且具有成本效益。意大利北部的 Serralunga d'Alba 葡萄园被选为验证目的的案例研究。在葡萄生长季节的四个不同时期获取的细化卫星驱动归一化差异植被指数 (NDVI) 地图，通过相关分析和方差分析显示，与原始数据集相比，更好地描述了作物的状况。此外，通过 K-means 分类器，从 NDVI 地图中有利地得出了 3 类葡萄园活力地图，这是种植者的宝贵工具。
