Pablo Picasso — Guernica, 1937. Museo Reina Sofia, Madrid.
Apollo AI
An AI-powered crop stress detection platform that classifies plant conditions from smartphone or drone imagery with a web-first, mobile-friendly workflow.
Description
Context
Apollo AI is an AI500 Hackathon 2025 Stage 2 submission focused on democratizing precision agriculture by making crop diagnostics accessible from standard phone and drone images.
The project is built as a deployed prototype, combining an interactive product experience with a production-style inference backend.
Problem and Objective
Farmers and field operators often lack quick, low-cost diagnostic tools that can be used directly in real-world conditions.
Apollo AI addresses this by providing:
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Fast image-based crop stress classification
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A low-friction user experience that works on mobile devices
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Multiple access paths (web interface and Telegram bot)
System Architecture
The platform is split into a modern frontend and a Python inference backend.
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Frontend: Next.js 15 (App Router), React 19, Tailwind CSS
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Backend: FastAPI deployed as serverless functions on Vercel
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Inference runtime: ONNX Runtime for efficient model execution
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Visualization: Three.js-based interactive 3D Drone Scanner
This architecture supports responsive UX, clear API boundaries, and deployment-ready scalability for prototype usage.
AI Model
The classification engine is based on a ResNet-18 model trained on PlantVillage data.
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Dataset scale: 70k+ images
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Target: crop stress and disease class detection
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Reported demo-class performance: up to 99% accuracy
The model pipeline is integrated into a user-facing flow so inference is not isolated from product behavior.
Product Experience
The frontend emphasizes usability for non-technical users and field-friendly workflows.
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Mobile-first responsive layouts
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Glass-morphism visual language for clear, modern UI
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Dedicated demo route (`/demo`) for testing classification flow
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Visual storytelling via 3D scanner interaction
Telegram Bot Integration
A Telegram assistant extends access for users who prefer messaging-first interactions.
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Provides instant analysis support and app access flow
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Uses a polling architecture
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Runs locally or on a VPS even when web deployment is on Vercel
This makes the platform more reachable across different user behavior patterns.
What This Project Demonstrates
Apollo AI demonstrates end-to-end product engineering across ML inference, backend API design, interactive frontend experience, and multi-channel delivery.
It also shows practical deployment thinking: model serving on serverless infrastructure, modern React architecture, and user-facing tooling designed for fast iteration in a hackathon-to-product trajectory.