Precision Agriculture



  • G.S. Paper 1,3

Why in news?

  • NITI Aayog has signed Statement of Intent (SoI) with IBM to develop precision agriculture using Artificial Intelligence (AI).

Precision agriculture:

  • Precision agriculture(PA) is also known as satellite farming or site specific crop management (SSCM).
  • It is a farming management concept based on observing, measuring and responding to inter and intra-field variability in crops.
  • The goal of precision agriculture research is to define a decision support system (DSS) for whole farm management with the goal of optimizing returns on inputs while preserving resources.
  • The first wave of the precision agricultural revolution will come in the forms of satellite and aerial imagery, weather prediction, variable rate fertilizer application, and crop health indicators.
  • The second wave will aggregate the machine data for even more precise planting, topographical mapping, and soil data.
  • Aims of Precision agriculture are to optimize field-level management with regard to:
    • Crop science: by matching farming practices more closely to crop needs (e.g. Fertilizer inputs)
    • Environmental protection: by reducing environmental risks and footprint of farming (e.g. Limiting leaching of nitrogen)
    • Economics: by boosting competitiveness through more efficient practices (e.g. improved management of fertilizer usage and other inputs)
  • Precision agriculture also provides farmers with a wealth of information to:
    • Build up a record of their farm
    • Improve decision-making
    • Foster greater traceability
    • Enhance marketing of farm products
    • Improve lease arrangements and relationship with landlords
    • Enhance the inherent quality of farm products (e.g. protein level in bread-flour wheat)

Precision agriculture using Artificial intelligence:

  • The practice of precision agriculture has been enabled by the advent of GPS and GNSS or artificial intelligence.
  • The farmer’s and/or researcher’s ability to locate their precise position in a field allows for the creation of maps of the spatial variability of as many variables as can be measured (e.g. crop yield, terrain features/topography, organic matter content, moisture levels, nitrogen levels, pH, EC, Mg, K, and others).
  • Similar data is collected by sensor arrays mounted on GPS-equipped combine harvesters.
  • These arrays consist of real-time sensors that measure everything from chlorophyll levels to plant water status, along with multispectral imagery.
  • This data is used in conjunction with satellite imagery by variable rate technology (VRT) including seeders, sprayers, etc. to optimally distribute resources.

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