Visual AI Agriculture Meetup - November 5, 2026

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

High-throughput industrial and agricultural inspection remains critically bottlenecked by the slow, subjective nature of manual classification, resulting in substantial economic losses worldwide. Traditional computer vision methods frequently struggle to scale or accurately isolate irregular, overlapping surface micro-defects.

To overcome these systemic limitations, computer vision researcher Saurav Upadhyaya developed a framework called 'SD-Vision', a two-stage deep learning framework that introduces an original class-weighted composite loss function to automate high-precision quantitative assessments. In this technical session, Saurav will demonstrate how his state-of-the-art architecture successfully orchestrates lightweight localization models alongside Shifted Window (Swin) Transformers to achieve instant, pixel-level injury segmentation.

Key Takeaways:

a) Multi-Stage Neural Orchestration: Concurrently deploying edge-optimized models (YOLOv8n) with advanced best-performing transformer (Swin-Base + UPerNet) for high-speed macro-localization and micro-injury mapping.

b) Mathematical Loss Optimization: Re-engineering a custom composite loss function and a balanced weighting schema to drastically mitigate over-segmentation and false positives.

c) Robust Real-World Deployment: Validating cross-sensor algorithmic resilience to transition complex frameworks from lab environments into real-time applications.

Wildfires are getting larger and more expensive every year. But caught early, a fire is a small job for a small team; caught late, nothing stops it. The cameras to catch them are already installed, so the work is not necessary more sensors but making the ones we have act.

This talk walks the whole chain: a small vision model running on the camera that flags smoke, an integration layer that scores the alert against terrain, weather and history to decide whether it matters, and a drone dispatched to confirm before anyone commits a crew. We will cover what has to run at the edge and why, how small the model can get before it stops seeing smoke, and what it takes to trust a detection nobody has looked at yet.

Environmental testing still begins with a person walking onto a site with a shovel. We built a system that turns a plain-language brief into a sampling campaign, flies it, and returns soil, water and vegetation samples with GPS, timestamps and chain of custody intact: 5,000 flights, roughly 9,000 miles and 570 flight hours over a 185-acre site in Roatan, much of it under canopy where GPS degrades and clearances are tight.

I will walk through the vision and planning stack, including multispectral and thermal canopy sensing, terrain and land-cover mapping, and the constraint solver that clears a route against airspace and battery budget before anything leaves the ground. Then the harder problem: evidentiary comparability.

A drone-collected result means nothing to a regulator unless it agrees with a hand-collected split under the same method, so I will show the dual-collection protocol we designed to test exactly that. I will close on why calibrated uncertainty and knowing when a model should refuse to answer matter more than raw accuracy once the output lands in a regulatory filing.