Brief description: 

Insurance in the agri-food sector deals with the increasing demand for agricultural insurance products and is expected to play a vital role in the forthcoming years as a tool for risk management. However, due to its multi-parametric nature, agricultural insurance is considered a special category in the insurance product portfolio. This innovation provides a solution for the insurance companies, to conduct fast and reliable damage assessments following weather-related disastrous incidents with the help of data-driven technological tools and services.  This set of tools and services aims to eliminate the need for on-the-spot checks for damage assessment and promote more rapid pay-outs. Through fusing heterogeneous data (EO, in-situ) the assessment of weather-related damages can be achieved even at field level. The convergence of the aforementioned technologies in a single dedicated framework is expected to deal effectively with insurance market demands which require a smooth transition from traditional insurance policies (expensive, require human experts for damage assessment) to more flexible index based insurance approaches. For the insurance company, and from a business perspective, this methodology could highlight the benefits that index-based approaches could offer, thus, leading to new insurance products. New smart farming products, combined with agri-insurance aspects, are another business perspective that arises for NEUROPUBLIC (NP), that leads the innovation activities.

Main Features: 

The DataBio Agri-Insurance solution involves the integration of high-power computing and EO-based geospatial data analytics for conducting damage assessment with data from IoT agro-climate stations for field-level condition monitoring. The solutions fully supports the gradual transition to index-based methodologies and correlates the impact of weather-related disastrous incidents (e.g. floods, heat-waves) that fall under the definition of the systemic perils to the EO-generated vegetation indices of specific crop types. The underlying reason for encouraging the aforementioned transition is that index-based insurance provides transparency and reduces bureaucracy since it is based on objective predefined thresholds. It has low operational costs requiring minimal human intervention. On the top of that, this new type of insurance can eliminate field loss assessment, adverse selection and moral hazards since the whole process is fully automated.

The solution includes:

  • an infrastructure of IoT sensor stations called Gaiatrons. Gaiatrons are telemetric agro-climate sensing stations, which are installed in the field and record atmospheric and soil parameters. Gaiatron measurements can be seen as the “starting point” of the damage assessment methodology as they confirm or not that potentially disastrous weather-related conditions are present at the area-of-interest (e.g. increased rainfalls, ambient temeratures, wind speeds)
  • a set of cloud-based services that combines the data collected from Gaiatrons with data from other sources (i.e. satellites images) and converts them into facts using advanced data analytics and machine/deep learning index-based techniques and
  • a web-based dashborad for high-level information visualization, that facilitates work (field level evaluation) prioritization and provides insights about the risk at which the insurance company is exposed to.
Areas of Application: 

Agriculture, Insurance 

Market Trends and Opportunities: 

The agri-food sector is constantly exposed to major risks threatening its viability. Production risks are among the biggest concerns of the agribusiness value chain as they relate to the uncertainty about the production levels that the farmers could reach following standard farming practices. The agricultural sector is extremely vulnerable to physical hazards (e.g., floods, hail) and biological threats (e.g., pests, diseases). Thereby, insurance in the agri-food sector deals with the increasing demand for agricultural insurance products and is expected to play a vital role in the forthcoming years as a tool for risk management. However, due to its multi-parametric nature, agricultural insurance is considered a special category in the insurance product portfolio. Difficulties in obtaining enough and valuable data for damage assessment, the complex biological processes that are incorporated in the crops growth stages, and the vast variability of production according to geographical criteria, creates an environment of great uncertainty that requires new techniques and expert knowledge.

When discussing agriculture insurance, an important notion to integrate is that the science concerns living systems, capable of resolving its issues or dying. In such, each crop season is unique and depends upon multitudes of factors (external and internal to each farm).

  • Historical field performance analysis: field analysis, soil type, intra-field variabilities, crop rotation, etc..
  • Season planning: sub-soil moisture, season weather forecast, crop prices, etc.
  • In season decision: best planting dates, agronomic prescriptions, seeding rates, etc.
  • Creating an index which takes all above-mentioned into account is a difficult if not impossible endeavor.


Crop production is increasingly becoming a data driven process, with producers making use of extensive sets of technology-driven tools which attempt to increase efficiency, output and ultimately profitability. The insurance industry and their products need to equally adjust to these evolutions and take advantage of the evolving technology available.

Agriculture concerns are living systems, seasonal dynamics and resilience:

  • Data quantity and availability do not mirror biological reality - only new methods and interlinks between data can approach agriculture realities on the ground.
  • An important part of product roll-out is driven by testing, as well as discussing the results with clients, which makes it a long-term undertaking.

New technologies are tools, not methods:

  • The development of a new tool does not change reality
  • An in-depth understanding of the socio-economic farming environment is key
  • Insurance needs to be in line with new farming trends and adjust their product design and offer accordingly
  • Data collection, storage and computing power are no longer a challenge - the application will be the differentiator
  • Data cleansing and validation is complex - outliers must remain the focus

New technology should not replace traditional methods, but enhance them:

  • The roles of agriculture insurance will evolve from a pure loss compensation role to become an advisory partner for producers.
  • “Boots on the ground” will remain a relevant part of agriculture insurance.
Customer Benefits: 

Provides the bases for new insurance products for the agricultural industry.  From perspective of insurance companies, the following KPIs are of relevance:

  • Accuracy in damage assessment
  • Decrease in required time for conducting an assessment.
Technological novelty: 
  • Neurocode tool from NP that allows the creation of the main pilot UIs in order to be used by the end-users (insurance company, farmers) and offering insights regarding weather-related perils. Neurocode handles data and visualizes them in highly informative UIs. Its TRL9 highlights its maturity and its ability to create flexible UIs on- demand addressing end- users needs.
  • GAIABus DataSmart Machine Learning Subcomponent from NP that:
    • Supports EO data  preparation and handling functionalities
    • Supports multi- temporal object-based monitoring and modelling for damage assessment 
  • GAIABus DataSmart Real-Time Subcomponent from NP that:
    • Performs Real-time data stream monitoring for NP’s Gaiatrons Infrastructure installed
    • Performs Real-time validation of data
    • Performs Real-time parsing and cross-checking
  • Georocket component from FRAUNHOFER that:
    • Has a Back-end system for Big Data preparation, handling fast querying and spatial aggregations
    • Has a Front-end application for interactive data visualization that would facilitate the work of an expert user
  • Neural network suite from CSEM that:
    • Exploits machine learning methodologies for crop type identification in order to be used for the detection of crop discrepancies at pixel level that might derive from reported weather-related catastrophic events


Component / Service / App


Mr Savvas Rogotis
BDV Reference categories: 
Data Analytics
Data Management
Data Visualisation and Interaction
Agriculture, forestry and fishing
Financial and insurance activities
Readiness Level: