Satellite based Analytics for Crop Planning

Satyukt

Optimal cropping pattern planning using integrated analysis of historical, real-time, and forecast data

top honours in National Awards for e-Governance 2020 conducted by the Government of India

10000
ACRES OF LAND
1
CROPS
1
COUNTRIES
1
LANGUAGES

Crop Health Monitoring is currently being tested by 10,000 farmers in U.P, India

Won a string of awards and grants, notably the BIRAC-Biotechnology Ignition Grant (BIG) of Rs 48 lakh, the first Social Alpha Quest for Agritech Innovations

Technology Brief

Satellites make earth-observation from orbit and provide the data at regular intervals in different spatio-temporal resolutions. The satellite with fine granularity good enough to monitor farms has been available since the mid-1970s. These data are available in huge quantities (Petabyte), which can be analyzed, processed, and interpreted to provide meaningful information on historical, real-time, and forecast data at the desired scale. Historical data such as soil moisture, vegetation indices (vis, NDVI, EVI, SAVI, etc), rainfall, and crop yield will be used to analyze the crop behavior at that location. Real-time data provides information on what is the ground reality at the present time. Forecast data such as weather, rainfall, or any likely risks provides what will be the situation during cultivation and post-harvest stage.

Benefits

At Satyukt, we have developed scalable algorithms that provide suggestions/solutions by doing an integrated analysis of historical, real-time, and forecast data to assess the suitability of agricultural land and crop analysis in each location. The algorithms combine multi-satellite imagery, weather forecast data, IoT sensors/gauges, and manual measurement data and will be analyzed using big data analytics, physical and machine learning methods to provide insights at the desired scale across the globe in different climatic conditions.

The significance of satellite-based optimal cropping pattern planning:

  • Farmers/stakeholders can obtain maximum profit by adopting the optimal cropping
  • Reduce the risk of crop
  • Proper sustainable utilization of available

Best price for the crop after harvesting.

technology readiness

MVP stage

Intellectual Property

Technology Not Protected

Certification

Not Applicable

We would like to add that our products are being used by premier institutes of India (IISc Bangalore, IIT Kharagpur) and abroad (CNRS, France and CEH, UK).

Technology Cost

Depending upon   the   total   subscription   area,   unit   cost   varies   from   60   INR/month/ha   to 5 INR/month/ha.

Target Customers

Target market customers are as following:

  • Irrigation companies such as micro/ smart irrigation systems and
  • Agri-input companies such as seed, pesticides, fertilizers and more
  • Agro-advisory companies
  • Post-harvest companies – Logistics, Distribution & Warehousing, Commodity Traders
  • Agri-output processing companies
  • Agri equipment/machinery companies
  • Food Companies – FMCG, Modern Retail, Convenience Food Chains, Cash & Carry
  • Agriculture Departments
  • Crop insurance companies
  • Insurance/reinsurance brokers
  • Flood insurance companies

Limitation

Currently, the limiting factor is the need to adapt content to vernacular languages to provide information to the farmers. However, solutions are available to quickly convert the information/advisory in the vernacular language.

Scalability

Satyukt has developed scalable algorithms by considering the various available earth-observation satellite constellations today to provide real-time data at the desired scale such as farm/regional. The algorithms have been built on scalable architecture and can provide scalable solutions across the globe in different climatic conditions. The algorithms combine and process multi-satellite data to deliver the agro-hydrological variables for over a million farms on daily frequency without compromising on end- user experience.

The output of the algorithms is far superior both in terms of accuracy in comparison to the traditional ones developed based only on the AI/ML as they do not work in areas other than the testing region.

The algorithms have been tested in several parts of India and across the globe viz. USA, UK, Australia, New Zealand, Spain and work very well in heterogeneous (higher variability in soil, crop, and farming practices) conditions. We feel that space-based data can add value to the decision making in data- sparse and heterogeneous regions.

Space organizations (e.g. ISRO, European Space Agency, NASA, and more) across the globe have launched various earth-observation satellites which revolve around earth from orbit for monitoring the earth’s features and changes and provide the different spatio-temporal resolutions raw data which are open source. The raw satellite data must be processed, analyzed, and interpreted according to the need for information of farmers and agribusiness stakeholders.

At Satyukt, we have the expertise and computation power to provide affordable data across the globe by leveraging advances in satellites, physical, machine learning, and in-house built scalable algorithms.

In India, we have tested in most of the states and have delivered various projects in Kerala, Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Gujarat, Rajasthan, Madhya Pradesh, Odisha, and West Bengal and have provided sample data for Haryana, Punjab, Chhattisgarh, Bihar, Uttar Pradesh, and Jharkhand.

Social Impact

An earth-observation satellite provides the greatest value in its ability to record a sequence of images to monitor earth features. Satellite data comprises signals sent by crops, soil, water, and more, at different wavelengths of light. Processing satellite images can let us know if any object is facing any problems because of the change in climate and weather. With satellite captured data, one can estimate historical and real-time agro-hydrological variables like health and growth of the crop, crop water stress/requirement/usage, and likely crop yield and more. Also, satellites provide the information to forecast rainfall and weather in advance. Integrated analysis of the above-mentioned data can provide insights into historical behavior and predict future uncertainties and help us to assess the real-time situation, hence tells crop suitability for the agriculture land. With these data at their fingertips, farmers/stakeholders can make optimal decisions and put favorable management techniques in place to minimize the inputs, maximize the outputs, and eliminate waste.

Small and marginal farmers with less than two hectares of land account for 86.2% of all farmers in India and cannot afford to install IoT sensors/fly drones to carry out farm management works or get insights of crops, weather, water, soil and more to make optimal decisions to improve the yield or predict the future uncertainties. As IoT sensors provide limited coverage (only a few cms around the sensor) of a farm with very limited variables, on the other hand, drones can cover a large area, however, most of them carry optical sensors which cannot provide accurate soil moisture. And both do not provide historical data to make the analysis or understand the crop/weather behaviours.

At Satyukt, we have developed scalable algorithms that provide suggestions/solutions by doing an integrated analysis of historical, real-time, and forecast data to assess the suitability of agricultural land and crop analysis in each location. The algorithms combine multi-satellite imagery, weather forecast data, IoT sensors/gauges, and manual measurement data and will be analyzed using big data analytics, physical and machine learning methods to provide insights at the desired scale across the globe in different climatic conditions at a much affordable price. Our subscription-based and customized plans allow farmers better information on their fingertips to get insights to make optimal decisions to choose suitable crops to sow and for better farm management to improve the yields.

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Team Kisanmitr

Email

kisanmitr@indiancst.in

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+91 9742979111