The incorporation of remote sensing in precision agriculture technologies has greatly helped to optimize the productivity and efficiency of both farming and agricultural production processes, especially in applications where the monitoring of an extensive area of land is required and the use of automation and robotics can help reduce the time and costs of it. The inspection of vineyard fields is one of the precision agriculture applications where remote sensing and deep learning techniques are combined into systems that can, autonomously, assess the state of the crop. Recent research incorporates satellite imagery as well as drone-captured images as inputs for Convolutional Neural Networks (CNN) capable of quickly processing them to output the information relevant to the specific application, ranging from the growth state of the plantation to a list of commands that an unmanned ground vehicle can use to move through the field.