Agriculture

AI is increasingly playing a very important role in supporting agricultural community maximize crop yield while optimizing costs. Deploying Geo-Spatial analytics and Computer Vision, some of the use cases in agriculture include:

Soil and crops health monitoring through: 

  • Deep Learning to identify soil nutrient deficiencies

  • Geospatial images and Computer Vision based mapping of fields, clusters and villages

  • Identification of man made versus natural crop damage

Precision farming through:

  • Deep learning driven guidance on optimum planting, water, crop rotation et al.

  • Decision support on harvest timing, nutrient management and pest attack countermeasures

  • Predictive Analytics based recommendations through data on temperature, precipitation, sunlight, wind speed

Farm insurance through: 

  • Geospatial analytics of current field conditions and forecast of risks based on past data

  • Fraudulent claim detection through Geospatial Analysis to detect extent of actual damage

Farmer chatbots - an NLP based virtual assistant to automate interactions with farmers:

  • Answer any general questions related to crop yield improvement among others

  • Provide various recommendations based on updated environmental data

  • Provide commercial details such as current market demand and prices

Advanced AI applications in agriculture including:

  • Autonomous tractors for automatically performing various tasks

  • AI driven robots to check crop quality, pick and pack crops and remove weeds

  • Drone monitoring of fields for various applications such as pests and soil quality