AI-Driven Precision Agriculture

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Issues / Challenges Involved in the Project
  1. To develop and validate AI/ML models for analyzing agricultural data (soil, weather, and crop health) to enable data-driven precision farming practices.
  2. To integrate multi-source agricultural data from IoT sensors, mobile cameras, and drone/satellite imagery for comprehensive field-level decision support.
  3. To optimize resource utilization (water, fertilizers, and nutrients) through precision agriculture techniques, thereby improving crop productivity and sustainability.
  4. To establish and evaluate key performance indicators (KPIs) for assessing the effectiveness of precision agriculture interventions in real-world farming environments.
  5. To promote sustainable agriculture and food security by minimizing environmental impact while enhancing yield and cost efficiency.
Team Lead
  • Suresh Kurumbanshi, Assistant Professor, Computer Engineering Department, MPSTME, Shirpur.

Suresh.kurumbanshi@nmims.edu

Team Members
  1. Dr. Nitin Choubey, Head- Computer Science Department, MPSTME, Shirpur.
    2. Nitin.choubey@nmims.edu
  2. Dr. Rajap Shiva Kumar, Assistant Professor, SVKMs School of Agriculture, Tardi, Shirpur.
    3. rajap.shivakumar@nmims.edu
  3. Dr. Venkatadri M., Associate Dean, MPSTME, Shirpur.
    4. venkatadri.marriboyina@nmims.edu


Team Lead