Discipline

Agri Data Science

4 published papers in agri data science.

Suresh Gawande, Anusha Raj, Mukund Dawale, Sagar Wayal, Kiran Khandagale, Indira Bhangare, Susmita Banerjee, Ashwini Gajarushi, Rajbabu Velmurugan, and Maryam Shojaei Baghini· Researcher · ICAR-Directorate of Onion and Garlic Research (DOGR), Pune, Maharashtra

YOLO-ODD: An Improved YOLOv8s Model for Onion Foliar Disease Detection

A field-image dataset of 1,000 onion plants trained an upgraded YOLOv8 detector that spots Anthracnose, Stemphylium blight, Purple Blotch, and Twister disease at 77.3% accuracy and 123 frames per second — fast and light enough to run inside a farmer-facing smartphone app.

Agri Data SciencePlant PathologyIndiaMachine LearningComputer Vision
Abhilash Singh Maurya, Bhartendu Yadav, Shubhi Patel, Roop Kumar, and Atin Kumar· Subject Matter Specialist, Agricultural Extension · Krishi Vigyan Kendra, Raebareli-II, Uttar Pradesh

Assessing the Role of Digital Platforms in Strengthening Agricultural Extension Services: Advisory to Empowerment

A survey of 400 Uttar Pradesh farmer households finds that regular users of digital advisory platforms — WhatsApp groups, YouTube, apps — score significantly higher on knowledge, empowerment, and yield than non-users, even after controlling for income and education.

Agri Data ScienceAgricultural ExtensionIndiaFarmer Empowerment
Victoria C. F. Westbrooke, A. Blake, A. Renwick, and S. M. Thomas· Researcher, Department of Land Management and Systems · Lincoln University, Christchurch, New Zealand

Bridging Gaps: A Study of Trust and Information Flow in New Zealand Agriculture

A qualitative study of 37 New Zealand dairy and sheep/beef farmers finds they increasingly trust informal, digital, and peer-driven information — including AI — over formal institutions, and are struggling with real information overload as a result.

Agri Data ScienceAgricultural ExtensionNew ZealandFarmer Decision-Making
Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, and Y. Narahari· Researcher · Indian Institute of Science, Bengaluru

Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation

A multi-agent AI system that generates season-long farming advice and checks it against a crop growth simulator before recommending it — in a decade-long test on maize in Karnataka, all three reasoning strategies beat static advisory guidelines, with the best approach lifting yields by over 1,150 kg/ha.

Agri Data ScienceMachine LearningAgronomyIndiaCrop Simulation