03 / Projects

One build. One win.
More to come.

We focus on quality and functional code. This catalog documents our verified build outputs, starting with our debut model.

LEAF_DIAGNOSTICS_01
System Figure 01 / Leaf Scan Vector
AAROH 2026 / 3rd in AI/ML Edge Inference

CropShield AI

CropShield AI is a lightweight computer vision script designed to evaluate agricultural leaf anomalies locally on edge setups. Instead of transmitting large image payloads over high-latency external networks, the inference is conducted locally.

We developed and packaged this build in a 48-hour sprint for the AAROH 2026 hackathon, where it was evaluated for its offline processing capabilities, clean user flow, and the direct link between its model predictions and interface displays, achieving 3rd position in AI/ML.

Scope Agricultural Anomaly Diagnostics
Win Venue AAROH 2026 (3rd in AI/ML)
Execution Local Deployment
The Pipeline

Future Roadmaps

These placeholder slots track areas we are currently evaluating as we prepare for upcoming competitive events.

01 / Researching

Edge Data Optimizations

Evaluating lightweight dataset pipelines that can run on low-bandwidth setups to track localized anomalies.

02 / Pending

Next Sprint Target

Reserved for our next 48-hour competitive build. Repository initialization will start once the next hackathon event theme is officially announced.