The objective of this project is to evaluate and analyze the potential of implementing a decision support system in vine and maize agriculture focused on optimal, efficient, fast, highly accurate and automated crop management, which will also contribute to lay the foundations for the development of a future system with predictive capacity focused on preventive measures.


The calculation and design of the system will be based on the development, application and validation of indices and remote sensing algorithms to eventually carry out a modelization of key biophysical variables in precision agriculture (moisture content, temperature,...), using radiative transfer models and data integration of different scales. The development of methodologies and algorithms that ensure progress in terms of accuracy, generalization and timeliness (speed) based on the development of more generalizable models, more accurate and faster and timely implementation is proposed. The project will also lay the foundations for the development of a future model based on prevention. These transfer models will be based on key biophysical variables in the development of vine and maize crops that can be detected and measured from RPAs and satellite platforms, eventually facilitating accurate, timely and automated agronomic management of crops.


This is an innovative proposal and of great interest as a business line given that, although the use of RPAS in precision agriculture has already made some progress, it is still necessary and relevant to carry out advanced research in notable aspects such as those related to the development of more generalizable and precise models as opposed to purely empirical ones. On the other hand, research into methodologies that allow the integration and generalisation of information from different platforms and sensors, with different potential (in terms of the information provided) and different spatial and spectral resolutions, is another of the current challenges that will also be addressed in this proposal.


The investment of Radiative Transfer Simulation Models (RTS) whose use from sensors located on RPAS has been scarcely explored so far. The use of RTSs and research into different investment algorithms will make possible both greater precision in modelling and a greater and better possibility of generalising the information provided by sensors located on RPAS platforms, which is a great challenge on the way to more precise and sustainable agriculture and will contribute to progress towards quasi-real time monitoring and even the development of preventive-type models.


Furthermore, there is a growing need to integrate and transform the digital data produced by the different platforms into interpretable and useful information for the end user, an aspect that has been little explored so far and which is expected to be the basis for the development of indicators and systems based on prevention (leading indicators) as opposed to indicators and systems of a reactive nature (lagging indicators). This aspect will be dealt with in this proposal in a transversal manner so that: i) the proposal will develop and apply methodologies for the integration of data, on different key indicators for the monitoring and management of crops, from sensors located on satellite platforms, RPAs and that provided by sensors located in the field; ii) all the data obtained from the different platforms and sensors will be integrated and interpreted, verified in the field (ground truthing) and transformed into information through the use of different methodologies and Geographic Information Technologies (GIT)