Monitoring the health of agricultural ecosystems from remote sensing data using semi-supervised neural networks.

The paper aims to create an understanding of the importance of remote sensing data and its efficient analysis. The authors show the benefits that the combination of unmanned aerial vehicles and deep learning algorithms can create. The focus of the paper is on a practical example: using a semi-supervised neural network and remote sensing data to distinguish between healthy and unhealthy trees. This scenario occurs in similar contexts in the real world and can provide added value for the observation of fruit trees and forest stands. Particularly in view of climate change and its effects, it is extremely important to ensure good health monitoring of ecosystems in order to initiate appropriate measures at an early stage. An Open-Source-framework serves as the basis. It is based on a semi-supervised approach and promises to create a good neural network with comparatively little annotated data. This work shows how Deepforest can be extended from simple tree detection to multi-class health monitoring. [ABSTRACT FROM AUTHOR]