NEWS – Agriculture Minister inaugurated the ICCC as a tech-driven solution to advance agricultural practices through informed decision-making

HIGHLIGHTS

What is the Krishi ICCC?

• Tech-based Solution: Utilizes multiple IT applications and platforms, including artificial intelligence, remote sensing, and GIS, to collect and process granular data related to agriculture.

Visual Output and Features

• Large-screen Display: Eight 55-inch LED screens showcase crop yields, production, drought situations, cropping patterns, trends, KPIs, insights, alerts, and feedback.
• Platforms Used: Includes the Krishi Decision Support System (DSS) for micro-level data processing and macro-picture presentation.

Objectives and Future Developments

• Comprehensive Monitoring: Integrates geospatial information from sources like remote sensing, weather data, soil survey, and farmer-related data to enable quick decision-making.
• Individual Farmer-specific Advisories: Future plans include generating customised advisories through apps like Kisan e-mitra using AI/machine learning algorithms and integrated data sources.

Practical Applications

• Farmer’s Advisory: Allows visualisation of soil carbon mapping, soil health card data, and weather-related data to provide authentic advisories on crop types and resource requirements.
• Drought Actions: Correlates yield changes with weather and rainfall data for proactive decision-making during droughts.
• Crop Diversification: Identifies regions suitable for diversified cropping based on crop diversification maps and field variability.
• Farm Data Repository: Krishi Decision Support System (K-DSS) acts as an agriculture data repository, aiding evidence-based decision-making and customised advisories.

Analysis: The Krishi ICCC represents a significant advancement in leveraging technology to enhance agricultural practices. Its comprehensive monitoring capabilities, individual farmer-specific advisories, and practical applications such as drought management and crop diversification are crucial for improving productivity, resource management, and decision-making in the agricultural sector. The integration of AI/machine learning and data-driven insights demonstrates a forward-thinking approach towards sustainable agriculture and farmer empowerment.