Remote sensing-based vineyard image segmentation with deep computer vision for precision agriculture

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. For both cases, a clear understanding of where the vineyard rows are located is crucial, so a system in charge of the vine identification is needed. This thesis aims to present a deep learning approach to solve the problem of segmenting vineyard rows in remotely sensed RGB images. The proposed goal has been achieved by implementing two different CNN that allow comparing traditional and cutting-edge architecture performances. Since, contrary to most studies in the field, that focus on particular cases, generality is a desired characteristic, a dataset consisting of aerial vineyard images of different grape varieties from several wine-growing regions was gathered to account for variable factors such as the illumination condition, the resolution of the images or the growth stage of the crop. Finally, the proposed solutions have been tested with images describing different scenarios with good results, for which a qualitative and a quantitative comparison is done. However, several issues can be further addressed to increase the model efficiency and performance, making this topic interesting for future work development.