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Agrihub INSPIRE Hackathon Starts with the Challenge #1 Crop Detection

Satellite Crop Detection technologies are focused on detection of  different types of crops on the field in the early stage before harvesting. Their classification is one of the key themes of the common agricultural policy within the initiatives of the European Commission. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops. and modern technologies in the issue of postprocessing of this kind of data sources. 

For detection of crops are usually used classification methods, which can be divided on:

  • Supervised classification is the technique most often used for the quantitative analysis of remote sensing image data. At its core is the concept of segmenting the spectral domain into regions that can be associated with the ground cover classes of interest to a particular application. In practice those regions may sometimes
  • In supervised classification the user or image analyst “supervises” the pixel classification process. The user specifies the various pixel values or spectral signatures that should be associated with each class. 

Image segmentation, which is defining directly object fields, can be considered as a more advanced method. Till now the method is working with Sentinel 2 and most of the solutions are working with data from one period.

The approach, which started to be tested during previous INSPIRE Hackathons, is based on classification selected indexes across all seasons starting from winter till end of the season. Using multitemporal data increases accuracy of classification.

Figure 1: Supervised classification

Another way, how can be increased accuracy of classification is to use unsupervised classification for preprocessing. Advantage of unsupervised classification is that division of objects is done not on training samples, but only on the base of phenological phase and spectral characteristics. During hackathons we started to prepare a method based on unsupervised classification. Disadvantage is that such image requires additional interpretation of classes.

Figure 2: Unsupervised classification without interpretation

The methods of unsupervised classification can be improved by segmentation algorithms.

Figure 3: Segmented Image

For the interpretation, data could be combined with existing LPIS data, which can also increase accuracy of classification. In a previous test combining LPIS data with interpreted images, we reached accuracy higher than 85 percent.

Figure 4: Combination of unsupervised classification with LPIS data

CHALLENGE GOAL: The focus of this challenge will be on extension of current experiments and turning these experiments into commercial businesses.

The registration for the challenges is open! Are you interested in extending current experiments available at Slovak Agrihub?

Register for this hackathon challenge


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The overall ambition is to design, develop and introduce a platform that connects people to the information. Firstly with integrating principles of social media like Blog, Forum, design Science Shop that enable to connect users with developers and researchers. And secondly by integrating different types of demo applications, where developers and researchers will have a chance to cooperate, test different APIs for new solutions and also provide common experiments.

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Plan4all Hub amazing tools. This tools are open for all users. Registered users are allowed to use these tools for own projects.


A library providing a foundation to build map GUI and extra components such as layer manager, permalink generating, styling of vector features, including OpenGIS® Web Map Service Interface Standard (WMS) layers to the map in a user friendly way.

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A tool for easy management, access control and publishing of vector based spatial data and their visualisations.

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Micka tool is a set of libraries and a web application for management and discovery of geospatial (meta)data.

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SensLog is an open sensor data management solution to receive, store, manage, analyse and publish sensor data.

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This is an extension of the geographic information system QGIS that enables to users create and edit layers and create map composition structures on local stations with a possibility to upload to the server.

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P4A DIH creators

The PlAn4all HUB is created in cooperation of 2 ENtities


Plan4all is a non-profit association sustaining and further enhancing results of multiple research and innovation projects in different specialisms. Such projects include spatial planning, transport, urban planning, environment, tourism and precision/autonomous farming.  Plan4all conducts research and experimental development and transfers results of such activities into practice.


Lesprojekt - služby s.r.o. is an innovative company with long-term experience with the implementation of research results into practice in the form of products and services. The company LESPROJEKT-SLUŽBY s.r.o. has a significant market position for more than 20 years and focuses primarily on business activities in the fields of environment, crisis management, forestry, agriculture and transport. The results of previous research activities are offered mainly in the form of services - eg SaaS (software as a service), PaaS (platform as a service) and IaaS (infrastructure as a service).




SmartAgriHubs is a € 20 million EU project under Horizon 2020 and brings together a consortium of more than 164 partners in the European agri-food sector. The project aims to realize the digitization of European agriculture by supporting an agricultural innovation ecosystem dedicated to excellence, sustainability and success.

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