I Built a Plant Disease Diagnosis App Using AI and Peer-Reviewed Science (Full Stack Tutorial)

Description

Description: Most AI apps are built on assumptions. This one was built on research. In this full build, you will create PlantCheck: a full stack plant disease diagnosis app where users upload a photo of a sick plant and get back an AI-powered diagnosis with confidence scoring, visual markers, differential diagnosis, and severity-conditional treatment recommendations. But before we write a single line of code, we run five targeted research queries through Consensus MCP to ground every product decision in peer-reviewed science. The disease taxonomy, the confidence display, the treatment logic, the pre-upload guidance — all of it traces back to published findings. By the end of this video you will have a working production app. But more importantly you will have a repeatable workflow for building with AI in a way that actually holds up. What You Will Learn Research-Grounded Product Development Using Consensus MCP to surface peer-reviewed research before writing code Translating research findings into concrete product decisions Building a technical spec grounded in published science rather than assumptions Core Setup and Architecture Next.js App Router project structure and routing patterns TypeScript interfaces for structured AI responses Tailwind CSS for building clean diagnostic UI components InsForge SDK setup for database and storage Database and Backend PostgreSQL schema design with enums and constraints InsForge database queries, inserts, and updates from the frontend Building API routes in Next.js App Router AI Analysis Pipeline Sending plant images to Claude Vision for diagnosis Designing structured prompts for consistent JSON diagnostic responses Confidence scoring and differential diagnosis logic Severity-conditional treatment recommendation strategy Image storage with InsForge Full Stack Integration Upload flow with multi-step loading UI Results page with three-state rendering: pending, error, and success Diagnosis history with persistent storage End-to-end testing and production verification Tech Stack ⚙️ Next.js (App Router) ⚙️ TypeScript ⚙️ InsForge (@insforge/sdk) — PostgreSQL + Storage ⚙️ Anthropic Claude Vision (@anthropic-ai/sdk) ⚙️ Tailwind CSS ⚙️ Consensus MCP Materials and References 🚀 Starter Repo: https://github.com/mendsalbert/plantcheck-starter 📖 Consensus Docs: https://consensus.app 📖 InsForge Docs: https://docs.insforge.dev 📖 Anthropic Docs: https://docs.anthropic.com 📖 Next.js Docs: https://nextjs.org/docs 👋 Social Media https://twitter.com/mendsalbert https://linkedin.com/in/mends-albert 💼 Business Inquiries albert.k.mends@gmail.com #Nextjs #TypeScript #InsForge #AnthropicClaude #AIApps #FullStackDevelopment #PlantDisease #ComputerVision #BuildInPublic #TechTutorial #ConsensusMCP #ResearchGrounded #WebDevelopment #Coding #React #TailwindCSS