The data-to-decision analytical tools provided by Niruthi serve various agricultural insurance stakeholders in a variety of ways.
Hyderabad-based Niruthi Climate and Ecosystem Services, an agritech company, is providing cost-effective solutions to increase crop productivity and reduce climate-related risks to marginal farmers and support governments in natural resource management.
By bringing together data from different sources—satellites, weather stations, drones, and internet of things (IoT) as well as farm-level data collection through mobile apps—to one single data technology platform it can provide a model of climate-risks and forecast crop yields that can be displayed in a visualisation dashboard. These farm-level insights can then be used by farmers, governments, banks, insurance companies, input and commodity markets.
Mallikarjun Kukunuri, CEO, Niruthi Climate and Ecosystem Services, says, “We plan to enhance our algorithms to work on crops and are working to create new services based on core technology to improve access to credit markets, allow financial transactions such as premium collection and claims payout through our CropSnap app, provide market linkages as well as offer smart farm management that is to use data and insights to minimise resource use and maximise yield.”
According to Kukunuri, the long-term objective is to create a platform for agriculture, serving the entire agro-ecosystem with farm-level insights that can be aggregated to meet various stakeholder needs from farmers to financial institutions. Using an array of tools and algorithms for leveraging data from satellites, mobile phones, drones, automated weather stations, collaborative computing and modelling, Niruthi’s technology efficiently deals with the heterogeneity common in marginal farming systems.
“We provide field-level insights using Big Data analytics and Artificial Intelligence (AI) for applications in remote sensing and climate analysis to improve rural livelihoods. The technology captures and combines fine-scale data from satellites, weather stations, and mobiles into sophisticated crop growth models empowered by advanced analytics,” he explains. It offers solutions for monitoring, modelling, and forecasting crop conditions, including location-specific weather, crop health, and crop yields by using technologies based on the terrestrial observation and prediction system.
Niruthi is led by a team of NASA scientists and retired scientists from IMD, CRIDA and agriculture and other universities. The team has a combined experience of over 300 years in remote sensing, agriculture, hydrology, ecology, climate science, and business development.
The data-to-decision analytical tools provided by Niruthi serve various agricultural insurance stakeholders in a variety of ways. One of the key requirements for increasing access to crop insurance as well as improving crop productivity is information about local weather conditions and forecasts that allow insurers to assess the basis risk and for farmers to react to changing weather conditions. Niruthi’s technology provides these local weather information by blending satellite data, solar radiation, rainfall and humidity with observations from a few, well-maintained weather stations. It demonstrated the technology in Maharashtra by creating village-level daily weather data for over 40,000 villages using data from four satellites and 300 weather stations.
Further, crop yield estimates serve as key inputs for insurance claim settlements as well as providing the basis for government agencies to decide minimum support price and assess food security. Historically, crop yields are estimated using crop cutting experiments. However, these estimates are highly unreliable because of the complexity in conducting such experiments and they are prone to manipulation.
The biggest challenge faced in India is lack of reliable data as well as access to many government data sets that are crucial for implementing farm-level solutions. “Often satellite data are seen as a panacea to lack of ground data. We believe this is a misconception. While satellite data certainly helps in scaling the solutions, robust solutions can only be relied upon when they are grounded by actual field data collected to represent extant conditions,” he adds.