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AgroData: Largescale Early / In-Season Crop-Type Mapping

AgroData: Largescale Early / In-Season Crop-Type Mapping

Pakistan’s agricultural sector, vital to its economy and the livelihood of millions, faces critical inefficiencies exacerbated by climate change and poor management. Despite being a leading producer of wheat, rice, and cotton, the country experiences significant crop wastage, up to 15% for wheat and over 20% for fruits and vegetables, due to inadequate planning, outdated data collection methods, and ineffective supply chain management. This inefficiency compromises local food availability and disrupts the balance between food imports and exports, leading to economic instability and increased reliance on costly imports. The primary issue is the absence of a centralized, up-to-date agricultural data system. The lack of real-time data hampers the optimization of crop production, resource management, and strategic decision-making, resulting in resource wastage and threats to national food security.

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This project introduces an advanced large-scale framework for early and in season crop-type mapping to enable crop yield prediction in Pakistan. There are significant threats to food security posed by population growth and climate change, which demand timely and reliable crop data for yield prediction and agricultural planning. Traditional methods relying on field surveys are slow and subjective. This project leverages quasi real-time satellite imagery and advanced deep learning methods to provide valuable crop-type data. The framework includes a distributed data hub, user-friendly access, AI-powered analytics, and predictive insights to support decision-making. Key components feature the AI prediction model, data ingestion pipeline, a frontend interface, and a predictive analytics dashboard. The aim is to visualize maps of different regions of Pakistan labeled by crop type and to enable yield estimation, monitoring of agricultural patterns, and assessing weather impacts, addressing food security and broader environmental concerns.

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Faculty

Students

  • Muhammad Umer Khan
  • Shalina Riaz
  • Syed Hashir Ahmad Kazmi

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