AI in Agriculture in India: Applications, Benefits, Challenges and Way Forward

Subject: GS Paper 3: Agriculture

Context: The rapid advancement of Artificial Intelligence (AI) is creating new opportunities to address structural challenges in Indian agriculture.

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About AI in Agriculture

• AI in Agriculture means the use of Artificial Intelligence technologies such as  sensors, and data analytics to improve agricultural activities, including crop monitoring, disease detection, weather forecasting, irrigation, input management, and yield prediction.

How AI Can Transform Agriculture

• Precision Farming: Enables data-driven management of crops and farm resources for more efficient agricultural practices.
• Climate-Smart Agriculture: Supports farmers in making timely decisions based on weather and climate information.
• Crop Management: Facilitates early identification of crop-related risks and supports timely farm-level interventions.
• Soil Management: Helps assess soil characteristics and crop requirements for better input management.
• Yield And Market Intelligence: Supports crop-yield estimation and informed production, marketing and post-harvest decisions.

India’s AI-Enabled Agriculture Ecosystem

• Digital Agriculture Mission: Launched in 2024 as an umbrella scheme for digital agriculture initiatives, it aims to prepare detailed soil profile maps at a 1:10,000 scale covering about 142 million hectares of agricultural land. It comprises of:
  • Agri Stack: Includes a farmer registry with a unique farmer ID, geo-referenced village maps linked with land records, and a Crop Sown Registry. As of 3rd August, 2026, more than 10.31 Crore Farmer IDs have been created across the country.
  • Krishi Decision Support System: Monitors crops, soil, weather, and water resources, including floods, droughts, and groundwater, using geospatial data.
• Bharat-VISTAAR: Bharat-VISTAAR (Virtually Integrated System to Access Agricultural Resources) is a multilingual, AI-powered Digital Public Infrastructure (DPI) providing farmers with personalised, location-specific agricultural advisories.
  • It was proposed in the Union Budget 2026–27 and Phase I was launched in February 2026.
• National Pest Surveillance System: Launched in 2024 and it uses AI-driven systems for early detection of pest infestations and crop diseases.
• Kisan e-Mitra: It is an AI-powered chatbot launched in 2023 for queries related to agricultural government schemes.

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AI Applications In Indian Agriculture

• Weather Forecasting:
  • Weather Information Network And Data System (WINDS): Develops Automatic Weather Stations (AWS) and Automatic Rain Gauges (ARGs) for hyperlocal weather data supporting crop insurance, advisories and disaster resilience.
  • AI-Based Forecasting: Models such as Pangu-Weather and GraphCast enable faster, localised forecasts; India also piloted AI-based monsoon-onset forecasting across 13 states during Kharif 2025.
  • Farmer Impact: 31–52% of farmers adjusted planting decisions based on SMS forecasts, highlighting the need for location-specific calibration and ground-station data for accuracy.
• Precision Farming And Crop Management:
  • Crop Health Monitoring: AI-enabled surveillance systems, including drones, can monitor crop health and detect diseases, pests and nutrient deficiencies.
  • Real-Time Agricultural Data: Satellite data, GPS, sensors and drones generate high-resolution, real-time information.
  • Resource Optimisation: These technologies can help reduce waste, optimise resource use and minimise environmental impact.
• Soil Health:
  • Soil And Crop Monitoring: AI and machine-learning tools can monitor soil and crop health-related parameters.
  • Soil Characterisation: Image processing can identify soil characteristics based on colour and texture.
  • Crop And Fertiliser Selection: This can help identify suitable crop types and required fertilisers.
• Yield Estimation And Market Optimisation:
  • YES-TECH: Used at the Gram Panchayat level for yield estimation and claim settlement under PMFBY. It covers paddy, wheat and soybean across 12 states.
  • FASAL: Provides pre-harvest production forecasts for 11 major crops across 20 states.
  • Supply Chain And Market Decisions: AI-led decision-support systems can provide information on production, price, demand and logistics to support post-harvest management and market decisions.

Challenges In Adoption Of AI In Indian Agriculture

• Data Quality: Fragmented and poor-quality data can affect AI accuracy.
• Algorithmic Bias: Inadequate representation of diverse farming conditions may create biased outcomes.
• Data Privacy: Concerns over farmer consent, privacy and data security.
• Digital Divide: Limited access to digital infrastructure and connectivity can constrain adoption.
• Small And Declining Farm Sizes: Over 86% of farmers have small landholdings; average size declined from 1.08 ha (2016–17) to 0.74 ha (2021–22).
• Affordability And Accessibility: AI services must be affordable, accessible and locally relevant, including through public platforms.
• Low Digital Literacy: Limited digital skills can hinder effective use of AI tools.
• Localisation: AI solutions may not adequately address local languages, crops, soils, weather and farming practices.

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Way Forward

• AI As A Public Good: Treat agricultural AI as a public good to ensure wider access to its benefits.
• Wider Rural Reach: Ensure AI innovations reach farmers in distant villages.
• Affordable And Locally Relevant Solutions: Ensure AI services are affordable, accessible and locally relevant, particularly for small farmers.
• Productivity And Climate Resilience: Promote large-scale adoption of AI-led systems to make Indian agriculture more productive and climate-resilient.

News Source: IE

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