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:
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Deep Learning to identify soil nutrient deficiencies
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Computer Vision based mapping of fields, clusters and villages
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Identification of man made versus natural crop damage through deep learning
Precision farming through:
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Deep learning driven guidance on optimum planting, water, crop rotation et al.
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Decision support on harvest timing, nutrient management and pest attack countermeasures
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Predictive Analytics based recommendations through data on temperature, precipitation, sunlight, wind speed
Farm insurance through:
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Predictive analytics of current field conditions and forecast of risks based on past data
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Fraudulent claim detection
Farmer chatbots - an NLP based virtual assistant to automate interactions with farmers:
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Answer any general questions related to crop yield improvement among others
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Provide various recommendations based on updated environmental data
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Provide commercial details such as current market demand and prices
Advanced AI applications in agriculture including:
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Autonomous tractors for automatically performing various tasks
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AI driven robots to check crop quality, pick and pack crops and remove weeds
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Drone monitoring of fields for various applications such as pests and soil quality