Agricultural risks

Agricultural Insurance for Latin America & Caribbean Sea

Guidelines design and implementation

"Parametric insurance has significantly reduced the impact of climate risks on agriculture, offering quick payouts for weather-related crop losses"

-Global Agricultural Insurance Trends

Discover the Future of Agriculture: Parametric Insurance

Parametric insurance is set to revolutionize precision agriculture in the next decade through risk management. According to a World Bank study, parametric insurance could help 50 million smallholder farmers in developing countries to protect their crops against climate risks, which could increase agricultural production by 10 per cent and reduce rural poverty by 20 per cent.

Our strategic objectives are centered on enhancing agricultural resilience and sustainability. First, we aim to deepen our understanding of disaster risks that threaten agricultural stability. Second, we are committed to strengthening risk governance to ensure robust management practices. Third, we invest in innovative ICTs to advance risk reduction strategies. Lastly, our focus extends to developing early warning systems for phytosanitary and agroclimatic events, known as EFAs, to provide timely alerts and safeguard crop health.

Augmented intelligence platforms

How we do it?

Equipments and devices

In the agricultural IoT industry, the following equipment and devices are used for optimizing farming operations:

Nodes: Deployed across fields to gather real-time environmental data.

Weather Stations: Measure conditions like temperature, humidity, and soil moisture.

Drones with Multispectral Cameras: Provide aerial views to monitor and manage crop health, detect pest infestations, nutrient deficiencies, and water stress.

These devices form an interconnected network that enhances agricultural practices and decision-making through accurate, data-driven insights.

Machine & Deep learning

Machine learning and deep learning are transforming agricultural practices through the following key applications:

Predictive Analytics: Utilizes historical data and algorithms to forecast future crop health, yield potentials, and environmental impacts.

Image Recognition: Deep learning models process images from drones and satellites to detect plant diseases, pest infestations, and nutrient deficiencies early.

Vegetation Index Analysis: Advanced algorithms assess data from multispectral images to calculate indices like NDVI (Normalized Difference Vegetation Index), which help monitor plant health and biomass.

These technologies not only increase efficiency and yield but also enable smarter, more sustainable farming decisions.

Financial risk & Services

Financial risk management and services play a critical role in the sustainability and profitability of agricultural operations through the following key mechanisms:

Parametric Insurance: Offers quick payouts based on predefined weather indices rather than actual loss assessments, providing immediate financial relief to farmers affected by adverse conditions.

Credit Services: Tailored loans and credit options enable farmers to invest in necessary equipment and seeds, ensuring they can sustain operations through varying economic climates.

Market Price Forecasting: Advanced algorithms analyze market trends and weather data to predict future commodity prices, helping farmers make informed decisions about when to sell their produce.

These financial tools are designed to mitigate risks and enhance decision-making, supporting farmers in navigating the complexities of agricultural markets.

IOT - IOB Networks

IoT and IoB networks are transforming agricultural practices by leveraging real-time data and behavioral analytics through the following key applications:

Sensor Networks: IoT devices distributed throughout the farm collect data on soil conditions, weather, and crop health, enabling precision agriculture.

Data Integration: Information from various sources is integrated to provide a comprehensive view of farm operations, enhancing decision-making and operational efficiency.

Behavior Analysis: IoB technologies analyze farmer and consumer behaviors to tailor agricultural practices and marketing strategies, optimizing resource use and product offerings.

These networks not only streamline farm operations but also foster a deeper understanding of the agricultural ecosystem, leading to more sustainable and productive outcomes.

Vegetation index analysis

Crop analysis trough multispectral imaging using drone technologies and augmented vision systems

Example application and implementation

Comprehensive Crop Health Analysis Using Multispectral and RGB Imagery with Deep Learning Integration

This application presents a detailed vegetation index analysis utilizing cutting-edge multispectral imagery captured by drones. By leveraging indices such as NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), and NDREVI (Normalized Difference Red Edge vegetation Index), we perform a thorough spectral analysis to assess the overall health of crops. These indices, derived from the multispectral data, provide crucial insights into vegetation vigor, chlorophyll content, and stress levels, allowing for precise monitoring of crop health.

In addition to spectral analysis, we employ DeepForest, a deep learning model, to analyze high-resolution RGB images of the crops. This model enables the identification of individual crop units within the field, facilitating a granular analysis of each plant's health. By combining the spectral indices with unit-level identification, we can accurately assess the health status of each individual crop, offering a detailed and comprehensive understanding of the field's overall condition. This approach not only enhances precision agriculture practices but also empowers decision-makers with the data needed to optimize crop management and improve yield outcomes.

Our team

Who are we?

Multidisciplinar research team

Innovative Collaboration to Enhance Crop Sustainability: DMU and EAFIT's Machine Learning Initiative Supported by the Royal Academy of Engineering

De Montfort University Leicester (DMU) is working with partner university EAFIT in Colombia to apply risk and machine learning concepts to improve crops' environmental and financial sustainability, preventing vast plant crops being lost to disease.

The Royal Academy of Engineering's Distinguished International Associates (DIA) programme is supporting the project, bringing together EAFIT University in Colombia and the Institute of Artificial Intelligence, through the Research in Societal Enhancement group at DMU with industry partners Avocado Crop Praga, UNIBAN, and UNIPALMA.

Our research will help small farmers get better access to finance and insurance and make small-scale agriculture more sustainable. It will as well contribute significantly to global food security by enabling better distributed and socially resilient farming practices.

Alejandro Peña Palacio

PhD. Information Management & Risks

Mario Góngora

PhD. RISE – Research in Societal Enhacement

Eduart Humberto Villanueva

PhD. Information Management & Risks

Maria Isabel Hernández Pérez

PhD. Natural Systems & Sustainability

Lina Maria Sepulveda

PhD. Information Management & Risks

Augmented intelligence

Platforms

Our Origins

Agricultural risks

AI Platforms

IA-AF Services

AI Risk Assesment

Agricultural risks

Discover the Future of Agriculture: Parametric Insurance