One of the necessary demands in extensive livestock systems is the counting of animals in areas of tens of hectares, costly when carried out manually and locally. In this context, this work proposes and discusses the efficacy of a semi-autonomous, non-invasive method for remote identification of animals in the field, applicable to precision livestock systems. The method was conceived from an exploratory research methodology based on remote sensing techniques that include image collection processes by aerial surveying with RGB camera embedded in unmanned aerial vehicle, persistence of images obtained by means of storage in space-time databases and processing of stored images for the construction of a rural property orthomosaic succeeded by the application of patterns discovery processes, making use of deep learning, especially convolutional neural networks. According to the experiments carried out, the method was effective, being able to identify and count animals from the collection of images made at 100 m height, with an accuracy of up to 95%, including the approximate geographical position of the animals to field.

中文翻译：

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VANT图像应用于动物监测的方法

大规模畜牧系统的必要要求之一是在数十公顷的区域内对动物进行计数，手动和本地进行时成本高昂。在此背景下，这项工作提出并讨论了一种半自主、非侵入性的方法在野外远程识别动物的功效，适用于精确的牲畜系统。该方法是从基于遥感技术的探索性研究方法中构思出来的，包括通过无人机中嵌入的 RGB 相机进行航空测量的图像收集过程、通过存储在时空数据库中获得的图像的持久性以及对存储图像的处理通过应用模式发现过程，利用深度学习，成功地构建了农村财产正射镶嵌，尤其是卷积神经网络。根据进行的实验，该方法是有效的，能够从 100 m 高度的图像集合中识别和计数动物，准确率高达 95%，包括动物到场的大致地理位置。