ABSTRACT

The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results.

摘要

对农民宣布的农作物进行早期和自动识别对于简化欧盟共同农业政策（CAP）的付款流程至关重要。当前，现场检查是局部的，昂贵的并且需要相当长的过程延迟。可以使用耕地的按时间顺序排列的卫星图像，以便神经网络可以形成申报作物的模型。一旦获得了农作物的模式，就可以系统地预测声明与神经网络模型的对应关系，并可以用于监视CAP。在本文中，我们提出了一个具有神经网络的学习模型，以训练一段时间内来自卫星图像的耕地像素为例。我们还建议在训练模型中使用几年来概括模式，而不将其与特定年份的气候特征联系起来。本文还介绍了该模型在学习烟草种植多年模式中的使用，并取得了很好的效果。