Deepspatial

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Agriculture

Background

In an effort to enhance the crop yield production and farmer income, a government agriculture department wanted to create an advanced agricultural platform using multiple technologies including IoT, communication networks, big data and artificial intelligence, to empower farmers of all sizes to increase their bottom line and optimize processes and decisions where needed.

Challenges

  • The initiative required multiple levels of approvals and validation within the client’s internal processes.
  • Integration of various data sets was necessary, including real-time data, mapping data, agricultural data from multiple sources, primary data collection across different zones, crop types, weather information, demographics, and soil information.
  • Data collection and validation were of utmost importance for an effective outcome.
  • Farmers had reservations about the new initiatives, uncertain about how their information would be used and whether it would bring any benefits.

How We Are Making An Impact

The Deepspatial platform helped integrate the various datasets and produced AI-driven insights which helped the department in calculating the evapotranspiration and crop water requirements, groundwater level, irrigation water requirements and much more. The platform’s AI-driven insights highlighted crucial parameters affecting the yield of the selected crops in real-time like maintaining soil pH level, checking soil temperature and moisture levels, and the life cycle of crops. Additionally, the platform produced insights and solutions for protecting crops from diseases by taking preventative measures and identifying them as soon as possible to prevent their spread.

Visible Results

  • Deepspatial’s platform facilitated the creation of a unique agricultural platform for the client.
  • The platform incorporated various technologies including IoT, Big Data, Technology Enabled Maps, Artificial Intelligence, and Interdisciplinary Sciences.
  • Detailed plans for water requirements in crops and irrigation were provided on a 10-day level advance forecasting with over 98% accuracy.
  • Maintaining the correct level of water at the right time resulted in cost savings and higher crop yields.
  • Additional features such as crop acreage, crop yield predictions, groundwater predictions, and Machine Learning-based crop classification were included in the system.
  • The platform provided farm-level visibility to the department.
  • Actionable insights on cross-functional factors like literacy, healthcare, and environmental requirements were shared with the client for a holistic view.
  • The insights helped in planning and improving production, leading to additional income for the farmers.