Computer Vision / Embedded AI / Edge Systems
Engineering vision for the real world.
I build intelligent systems that combine computer vision, embedded deployment, and product-grade interfaces into work that is both technically rigorous and ready to use.
I work across the full stack of modern product building: strategy, interface design, front-end engineering, and AI-powered functionality delivered as cohesive user-facing systems.
Built Projects
Across AI systems, web platforms, and experiments
Core Disciplines
AI, front-end, full-stack, and product execution
Execution Focus
Designing for polished, portfolio-ready delivery
Selected Work
Projects that connectdesign, code, and AI.
A portfolio of systems work spanning intelligent interfaces, full-stack applications, embedded experiments, and product-focused technical execution.
01
AgriSense
AI product design grounded in real-world constraints
A retrieval-augmented assistant that combines applied research, inference design, and a user-facing product layer into a clear diagnostic workflow.
0%
Accuracy
<0s
Response
0%
Offline
From software products to edge devices...
02
ESP32 Leaf Disease Scanner
Computer vision compressed into deployable hardware
An edge ML build focused on performance, cost, and legibility. The work covers model optimization, embedded constraints, and a system people can operate in the field.
0ms
Inference
0$15
Cost
0%
Accuracy
From embedded systems to operational dashboards...
03
ARMS - Agricultural Resource Management System
Full-stack product thinking with measurable utility
A platform build that blends interface architecture, application logic, and decision-support tooling into a system with practical day-to-day value.
0+
Farms
0%
Time Saved
0%
Prediction
From systems thinking to shipped experiences...
04
Realitech - Smart Irrigation Controller
Connected infrastructure with a product mindset
A hardware-software system that shows how sensing, automation, and UX can come together as one coherent service layer.
0%
Water Saved
0%
Uptime
0mo
ROI
Need someone who can think through both product and implementation?
Let's talkMy practice spans the full pipeline: from training neural networks in Python and PyTorch, to quantizing models for ESP32 microcontrollers, to building the web interfaces that make it all accessible. I work at every layer of the stack because real agricultural AI demands it.
Research
AgriSense: Retrieval-Augmented LLM System for Tomato Disease Diagnosis
International Conference on Agricultural AI, 2025
Edge-Optimized Deep Learning for Real-Time Leaf Disease Detection on ESP32
IEEE IoT Journal, 2024
Precision Irrigation Using IoT Sensors and Machine Learning
Smart Agriculture Workshop, 2024
* Placeholder publications — replace with your actual research papers
Contact
Let'sbuildsomethingthatgrows.
I'm open to AI engineering roles, research collaborations, and consulting in agricultural technology. Reach out directly.
© 2026 Robert Jhon Aracena
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