import React from "react"; import Link from "next/link"; import type { Metadata } from "next"; import { APP_NAME, BETA_DISCLAIMER } from "@/lib/constants"; export const metadata: Metadata = { title: "About", description: `Learn about ${APP_NAME} — our mission, methodology, data sources, and limitations. Open-source plant disease identification.`, }; /* ─── FAQ accordion (server component with details/summary) ─── */ const faqs = [ { q: "How accurate is the disease identification?", a: "Our model has been trained on 50K+ labeled plant disease images covering 25+ plant species. Accuracy varies by plant and disease type, with confidence scores provided for each diagnosis. The model performs best on common diseases with visible foliar symptoms. We recommend using multiple sources of information for critical plant health decisions.", }, { q: "Which plants are supported?", a: "We currently have detailed disease data for 8+ common garden plants including tomato, basil, rose, monstera, snake plant, bell pepper, lavender, and sunflower. We are actively adding more plants. Browse our full catalog on the Browse Plants page.", }, { q: "Can I use this for commercial farming?", a: "While our database covers common agricultural plants like tomatoes and peppers, the tool is designed primarily for home gardeners and small-scale growers. For commercial agriculture, we recommend consulting with local extension services, certified crop advisors, and using laboratory testing for definitive diagnosis.", }, { q: "How does the AI model work?", a: "The model uses a convolutional neural network (CNN) trained on thousands of labeled plant disease images. When you upload a photo, the model analyzes visual patterns — leaf spots, discoloration, wilting patterns, and other symptoms — then matches them against known disease signatures. The output includes the most likely diagnosis with a confidence percentage.", }, { q: "Is my data private?", a: "Yes. Uploaded images are processed temporarily for analysis and are not permanently stored or used for training without explicit consent. We do not share or sell user data. See our privacy policy for details.", }, { q: "What if my plant has a disease that's not in the database?", a: "If the model cannot identify the disease with sufficient confidence, it will indicate that the condition is not recognized. In this case, we recommend consulting a local plant pathologist, master gardener, or agricultural extension service. You can also contribute by submitting data to help us expand our knowledge base.", }, ]; function FAQAccordion() { return (
{faq.a}
Making plant disease identification accessible to every gardener.
Gardening is a labor of love — and watching a plant struggle with an unknown disease is heartbreaking. Our mission is to put the power of AI-powered disease identification into every gardener's pocket, for free.
{APP_NAME} was built by a team of gardeners and developers who were frustrated with vague, generic plant disease advice. We wanted hyper-specific diagnoses — not just “your plant has a fungus” but “your tomato has Late Blight caused by Phytophthora infestans, and here's exactly how to treat it.”
The identification engine uses a deep convolutional neural network trained on a dataset of 50,000+ labeled plant disease images spanning 25+ plant species. When you upload a photo:
Our disease knowledge base is curated from peer-reviewed plant pathology resources, including:
We prioritize evidence-based, actionable information. Disease descriptions, treatments, and prevention tips are reviewed for accuracy before inclusion.
{BETA_DISCLAIMER}
The AI model may not accurately identify all diseases, especially unusual presentations, early-stage infections, or diseases outside its training data. Always confirm diagnoses with professional resources for critical decisions.
This tool is not FDA-approved or certified as a medical/agricultural diagnostic device. It is an educational assistive tool.
{APP_NAME} is free and open source. We believe plant health information should be accessible to everyone. The entire project is available on GitHub, and we welcome contributions!
You can contribute by:
View on GitHub →