OM DENNE EPISODE
F1 scores weren't built for farms with cottages, livestock, and bore wells on the same parcel. Praveen Pankajakshan and Deepa Karthykeyan (Athena Infonomics) join us live for real-time Q&A and first access to new insights rethinking AI benchmarking for smallholder agriculture.
Praveen explains:
◼️ Why do F1 and BLEU scores miss what actually matters on smallholder farms
◼️ What happens when a crop identification model meets cottages, roads, and livestock in one parcel
◼️ How can agriculture borrow a gold standard from the health sector
◼️ Why does no one fund the localization smallholder AI actually requires
◼️ What metric could replace F1 once trust and equity enter the equation
◼️ How do you benchmark a model after deployment not just before
◼️ Why does building AI capacity in Indian states matter for agricultural benchmarking
◼️ What happens when human evaluators disagree on the same model output
00:00:05 | Make benchmarking matter for advisory tools
00:04:31 | Test models against farming diversity
00:08:00 | Move beyond controlled model certification
00:11:12 | Rethink benchmarking after real deployment
00:12:50 | Challenge crop models on messy parcels
00:17:09 | Ask who funds true localization
00:19:46 | Compare outputs across multiple evaluators
00:23:42 | Look for agriculture's gold standard
00:25:21 | Watch governments build AI capacity
00:30:54 | Invent metrics beyond F1 scores
To read the discussion papers, click the link below.
