India AI Impact Summit 2026

indo-canadian-symposium-on-ai-for-food-energy-and-health-sectors

7 min read

TRACK:Economic Growth and Social Good
SPEAKERS
  • Dr. Ajay Dalai, University of Saskatchewan
  • Dr. Baljit Singh, University of Saskatchewan, Canada.
  • Dr. Debi Prasad, CSIR-IMMT, Bhubaneswar
  • Dr. Maneesha Vinodini Ramesh, Amrita Vishwa Vidyapeetham
  • Dr. Mukesh Saini, IIT Ropar
  • Dr. Rajeev Ahuja, IIT Ropar
  • Dr. Steve Shirtliffe, College of Agriculture and Bioresources at the University of Saskatchewan.
  • Prof. Ajay Kumar Dalai, University of Saskatchewan, Canada
  • Prof. Suman Kumar, IIT Ropar
SESSION OVERVIEW

Around the world, companies and universities are racing to develop large Foundation AI models tailored to these sectors. This session will focus on a series of talks on the application AI for food, energy and health, followed by discussion to arrive at summary points for sharing with the organizers. Focus points will be on driving advancements in forecasting and logistics, boosting efficiency and automation, and enhancing product quality across the entire value chain.

VIDEO RECORDING

Indo-Canadian Symposium on AI for Food, Energy and Health Sectors

Abstract

The panel brought together senior researchers from Canada and India to map the current AI landscape for agriculture, energy and health, and to explore concrete pathways for Indo‑Canadian collaboration. Opening remarks highlighted India’s rapidly growing AI ecosystem and the substantial private‑sector investment backing it. Each speaker then presented a focused research portfolio—ranging from AI‑enabled agri‑tech startups at IIT Ropar, to biomass‑to‑fuel conversion in Saskatchewan, to AI‑driven mineral‑extraction at CSIR‑IMMT, to community‑centric compassion‑driven research at Amrita, and finally to end‑to‑end AI solutions for Indian farms at Annam.ai. The session concluded with audience questions on climate‑resilient farming, the need for near‑perfect AI accuracy in food and health, and the logistical challenges of operating in harsh northern climates.

Detailed Summary

  • Moderator opened by framing the “India AI Impact Summit” as more than an event; it is an impact‑driven program.
  • Cited the Stanford Global AI Report ranking India’s AI ecosystem as the world’s third‑largest in vibrancy.
  • Mentioned that GitHub hosts the second‑largest AI project repository from India.
  • Investment figures: ≈ 15 bn invested by Google in the last year, $11 bn pledged by Tata.
  • Stressed the need for policy‑ready AI and a multilateral panel that bridges research, industry and government.

Key Insight: The rapid funding surge and open‑source contributions give India a strong foundation, but coordinated policy and cross‑border collaboration are required to translate this into sectoral impact.

2. Vision for Indo‑Canadian Collaboration

  • Prof. Rajeev Ahuja (IIT Ropar) framed the symposium as a “win‑win” platform, citing:
    • IIT Ropar’s AI Centre of Excellence (CoE) for Agri‑Tech.
    • 30 % of India’s deep‑tech agri startups originate from IIT Ropar.
    • Saskatchewan’s reputation as a major agri‑province, with strong Indian diaspora ties.
  • Proposed joint research pilots, faculty exchange, and policy dialogues between Canada’s Ministry of Agriculture and India’s AI Mission.
  • Highlighted upcoming visits by Canadian officials (provincial premier, prime minister) to support bilateral initiatives.

Recommendation: Establish a bilateral steering committee to prioritize pilot projects in precision agriculture, biomass conversion, and AI‑enabled health monitoring.

3. Speaker Presentations

3.1 Dr. Ajay Dalai – Saskatchewan’s AI Opportunities in Food, Energy & Health

  • Described Saskatchewan: 1.3 million people, 13 % of Canada’s land, ≈ 48 % of national agricultural acreage.
  • Noted vast natural resources – 25 % of world’s uranium, 30 % of global potassium, abundant oil & gas.
  • Food & Agriculture: Province supplies ≈ 85 % of Bangladesh’s chickpeas & green peas.
  • Biomass Challenge: Global residue production ≈ 200 bn t/yr → methane emissions if unmanaged.
  • Research focus: Converting heterogeneous biomass into hydrogen, transportation fuel, aviation turbine fuel; aligning with Canada’s net‑zero by 2050 target.
  • Emphasised knowledge gaps: Canada has raw minerals, India has advanced processing tech.
  • Mentioned human‑resource constraints (visa bottlenecks for Indian students) and recent diplomatic progress with the Canadian ambassador.

Key Insight: AI can model heterogeneity of biomass, optimise conversion pathways, and bridge the resource–technology gap between the two countries.

3.2 Dr. Debi Prasad – AI for Critical‑Mineral Exploration & Sustainable Materials

  • Introduced CSIR‑IMMT, a premier Indian lab (est. 1942) focusing on mineral extraction, AI‑enabled instrumentation, and new‑material design.
  • IMMT is recognised by the Ministry of Mines as a Centre of Excellence for Critical Minerals.
  • Highlighted the scarcity of critical minerals for EVs and renewable‑tech supply chains.
  • Discussed AI‑ML pipelines for:
    • Mineral‑processing optimisation (reducing time & resource use).
    • Exploration modelling (predictive mapping of ore bodies).
    • Instrument design (AI‑driven sensor development).
  • Mentioned CSIR’s Jigyansa program (AI outreach to school children) and PhD pathways to cultivate AI talent.

Announcement: Ongoing AI‑mission projects across CSIR labs are receiving dedicated funding; collaborations with Canadian partners are being actively sought.

3.3 Prof. Maneesha Vinodini Ramesh – Compassion‑Driven, Community‑Centric AI

  • Presented Amrita’s “Live‑in‑Labs” model: students and faculty co‑live with rural communities to identify genuine challenges.
  • Described the SREE digital platform that aggregates: water quality/quantity, sanitation, disease prevalence, food availability, and links them to AI‑driven decision support.
  • Emphasised that AI alone is insufficient; it must be augmented by local knowledge (human intelligence).
  • Health work:
    • Remote patient monitoring across 14 Indian states (200+ communities).
    • Tele‑diagnosis hubs linking 10 campuses, 35 k students, 2 600‑bed hospitals (Faridabad) and 1 400‑bed hospital (Kochi).
    • Focus on malnutrition surveillance.

Key Insight: Embedding AI within interdisciplinary, compassion‑driven ecosystems ensures relevance, equity, and scalable impact.

3.4 Dr. Mukesh Saini – Annam.ai’s End‑to‑End AI Solutions for Indian Agriculture

  • Mission: Build a Centre of Excellence (₹300 cr) under the Ministry of Education; deliver practical AI tools to farmers.
  • Hardware stack: Custom weather stations, soil sensors, and farm‑level data acquisition boards (fully in‑house PCB design).
  • Farmer‑Facing LLMs: Language‑model chatbots in regional languages delivering crop‑specific advisory.
  • Computer‑Vision Suite (four pillars):
    1. Crop Identification – 99.2 % accuracy on 3 M field images; essential for downstream advisory.
    2. Pest Detection – Synthetic data generation using 3‑D pest models; 94 % accuracy on 25 pest classes.
    3. Disease Detection – 92 % accuracy using AI‑augmented imaging.
    4. Damage Assessment – Mobile‑image‑based estimation of plant count & injury for insurance claims.
  • Food‑Adulteration Work: Novel thermal‑imaging method for detecting adulterated turmeric by monitoring dielectric‑constant changes; prototype presented at ACM Multimedia conference.

Recommendation: Scale the field‑tested crop‑ID model to the Ministry of Agriculture pipeline; fast‑track the turmeric adulteration prototype toward commercialization.

3.5 Dr. Rajeev Ahuja – IIT Ropar’s Agri‑Tech Startup Ecosystem

  • Reiterated IIT Ropar’s AI CoE for Agri‑Tech and its deep‑tech startup concentration (≈ 30 % of India’s agri deep‑tech startups).
  • Cited successful spin‑outs and the AI‑mission funding landscape (₹10,000 cr+ deployed since April 2024).
  • Stressed need for strategic, sharp collaborations—particularly with Canadian academic and industry partners—to accelerate deployment of prototypes.

Open Question: How to align venture‑capital timelines with the policy‑driven AI mission to ensure sustainable scaling?

3.6 Dr. Steve Shirtliffe – (Mentioned only)

  • No active presentation; referenced as a senior faculty member from the University of Saskatchewan’s College of Agriculture & Bioresources.

4. Audience Q & A

QuestionRespondent(s)Core Points
Climate‑hardiness of Saskatchewan agriculture – how can AI mitigate extreme cold and short growing seasons?Dr. Ajay DalaiClimate change is global; Canada must integrate renewable energy (bio‑energy, nuclear, solar) with AI‑optimised resource planning. AI can forecast optimal planting windows, model biomass conversion, and improve energy mix during long winters.
Ensuring AI accuracy in food & health – is 90 % sufficient?Dr. Mani​sha Ramesh (followed by moderator)For life‑critical sectors, near‑100 % accuracy is mandatory; any failure threatens a whole crop cycle. Emphasised the need for rigorous field validation and bias mitigation (especially urban‑vs‑rural data).
Capturing farmer wisdom – how to avoid urban‑centric AI bias?Dr. Mani​sha RameshAI models must be trained on farmer‑generated data, incorporating local resilience practices. Community‑driven data pipelines (e.g., “Live‑in‑Labs”) are essential.
Policy & standards – what governance is needed for cross‑border AI deployment?Moderator (summarising later)Calls for standardisation frameworks, mutual recognition of AI certifications, and joint funding agreements.

5. Closing Remarks & Administrative Notes

  • Moderator thanked panelists, highlighted the success of the Indo‑Canadian partnership model, and invited a group photo.
  • Mentioned upcoming parallel programs: India‑Korea AI initiative, India‑Israel diplomatic AI dialogue.
  • Noted that Prof. Baljit Singh had to leave early for the India‑Korea session; his earlier work underpinned the symposium.

Key Takeaways

  • India’s AI ecosystem is now the world’s third‑largest in terms of vibrancy and open‑source contributions, backed by $30 bn+ of recent private investment.
  • Saskatchewan offers a unique test‑bed: vast agricultural land, abundant mineral resources, and a net‑zero‑by‑2050 ambition—perfect for AI‑driven biomass‑to‑fuel research.
  • Critical‑mineral AI (CSIR‑IMMT) can accelerate India’s EV and renewable‑tech supply chains; cross‑border collaboration can pair Canada’s ore wealth with Indian AI‑driven processing.
  • Compassion‑driven, community‑embedded AI (Amrita) demonstrates that human intelligence must complement algorithmic models for equitable impact.
  • Annam.ai’s end‑to‑end pipeline (hardware → LLM advisory → CV‑based crop/pest/disease detection → damage assessment) is field‑validated on >3 M images and ready for large‑scale rollout.
  • IIT Ropar’s agri‑tech startup hub supplies a significant share of India’s deep‑tech ventures, offering a pipeline for joint commercialization with Canadian partners.
  • Accuracy demands in food, health, and energy applications are near‑perfect; robust validation, bias mitigation, and community data are non‑negotiable.
  • A bilateral steering committee and standardisation framework are essential to align research, policy, and industry across the two nations.
  • Cross‑border exchanges (faculty visits, joint pilots, policy workshops) are already underway and will be the engine for future collaborations.

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