cropguard-dataset-kit

A complete crop disease detection front end in one HTML file — no install, no build step, no server required to open it. A user takes or chooses a photo of a crop leaf and immediately gets a disease name, a confidence score, a severity level (with colour), an urgency level, ordered treatment steps and suggested products.

It is the most accessible deliverable in the project: email the file, host it on any static link, or open it straight from a phone's storage. It works on a basic Android phone with no app-store download.

The three files

File	Language	Notes
`cropguard.html`	Bilingual	English ⇄ Twi toggle in the header. This is the main app.
`cropguard-en.html`	English only	Same app, no toggle — for English-only deployment.
`cropguard-tw.html`	Twi only	Same app, no toggle — for Twi-only deployment.

All three are byte-for-byte self-contained: HTML, CSS and JavaScript in a single file, fonts from Google Fonts, no other external dependencies.

Coverage

14 Ghanaian crops, 55 disease/healthy classes: maize, cassava, tomato, cocoa, cashew, plantain, yam, pepper, cowpea, groundnut, rice, okra, garden egg, mango.

2. How to use it

Open the file in any modern browser (phone or computer) — double-click it, or host it and visit the link.
Take a photo ("Take a photo of a leaf") or Choose from gallery. On a phone the camera button opens the rear camera directly.
Check the photo on the preview screen — the app reminds the user to fill the frame with the diseased leaf in good light — then tap Analyse crop.
Read the result: crop + disease, confidence bar, severity badge (green/amber/red), urgency, a numbered treatment list (most urgent first), suggested products and a short "about this disease" note.
Scan another leaf or Share result (uses the device share sheet, or copies to clipboard).

A Help sheet on the home screen explains all of this to first-time users, in the selected language.

3. The four-screen flow

The app is a single-page application with four screens (home → preview → loading → result) toggled by a small show() function — there is no routing library and no page reload.

 HOME ──tap camera/gallery──▶ PREVIEW ──tap Analyse──▶ LOADING ──result ready──▶ RESULT
   ▲                                                                               │
   └───────────────────────────── "Scan another leaf" ────────────────────────────┘

4. How the diagnosis works — two modes

The app is designed to never be broken, so it has two inference paths and picks automatically:

Mode A — Connected to the trained model (accurate)

If an API URL is configured, the app sends the photo to the backend (POST /predict) and shows the real CNN prediction. This is the accurate mode and the one to use for real diagnosis. Set it by either:

running localStorage.setItem('cropguard_api', 'https://your-server:8000') in the browser console, or
hosting the app and pointing it at your deployed FastAPI server (see the backend documentation).

The footer shows "Connected to AI model" with a live dot when an API URL is set.

Mode B — On-device estimate (offline fallback)

When no API URL is set, the app runs a genuine on-device image analysis in JavaScript using an HTML <canvas>. It:

draws the photo to a 180×180 canvas and reads the pixels,
drops near-white background pixels,
measures the proportion of green, yellow, brown and dark leaf pixels,
converts that into a diseased-area ratio, which sets the severity (early < 0.20 ≤ moderate < 0.55 ≤ severe), and
maps the dominant colour signature to a likely disease among common Ghanaian field patterns.

The footer shows "On-device analysis" in this mode.

Be honest about Mode B. It is a real colour/lesion heuristic, not the trained model, and it is not the ~98% system. It is a reasonable offline estimate and a graceful fallback so the app still does something useful with no connectivity — but accurate, all-crop diagnosis comes from Mode A (the connected CNN). The on-screen confidence in Mode B is an estimate, and the app's disclaimer says clearly that this is a diagnostic aid, not a replacement for an extension officer.

5. Bilingual design (`cropguard.html`)

Everything the user sees exists in both English and Twi. The header toggle calls setLang('en' | 'tw'), which:

stores the choice in localStorage (cropguard_lang) so it persists,
swaps every UI string (elements tagged data-i="key" are filled from the language table),
rebuilds the crop "pills" list in the chosen language,
re-renders the current result in the new language if one is showing.

Data model

All content lives in one JavaScript object, DATA, injected into the file:

const DATA = {
  crops:    { en: [...14 names...], tw: [...14 names...] },
  t:        { en: {...UI strings...}, tw: {...} },     // labels, buttons, help text
  severity: { en: {early/moderate/severe...}, tw: {...} },
  help:     { en: [[title, body], ...], tw: [...] },
  diseases: {
    "cocoa_blackpod": {
      crop: { en: "Cocoa", tw: "Kookoo" },
      name: { en: "Cocoa Black Pod", tw: "Kookoo Aba Tuntum Yare" },
      cause:{ en: "...", tw: "..." },
      tx:   { en: ["step 1", ...], tw: ["..."] },       // treatment steps
      chem: { en: ["Copper hydroxide", ...], tw: ["..."] } // suggested products
    },
    "maize_healthy": { crop:{...}, name:{...}, healthy: true },
    ...
  }
};

The single-language files (-en / -tw) use the same structure flattened to one language and have no toggle.

The Twi agronomic text was written carefully, but for the more specialised crops (cocoa, cashew, plantain, yam, etc.) a local agricultural extension officer or MoFA/COCOBOD agent should review it before farmer-facing release — some disease terms have no settled Twi equivalent.

6. Severity and urgency

Severity is derived from the diseased-area ratio (Mode B) or returned by the server (Mode A) and is shown with a colour and an urgency level:

Severity	Colour	Urgency	Meaning
Early stage	🟢 green	Routine	Small/localised (<~20% of leaf). Act soon.
Moderate stage	🟠 amber	Urgent	~20–60% of leaf; can spread fast. Treat this week.
Severe stage	🔴 red	Emergency	>~60% affected. Act today.

Healthy results skip severity and show a "no disease detected — keep monitoring" panel instead.

7. Privacy

No sign-up, no account, no analytics, no tracking. In Mode B the photo never leaves the device. In Mode A the photo is sent to the configured server only for the single prediction and is discarded there (the backend never writes it to disk). This matches the privacy-by-design intent of the report (§3.10/§3.13).

8. Deploying the HTML app

Because it is one static file, hosting is trivial. Any of these work:

Email / copy: send cropguard.html; the recipient opens it on their phone.
Static host: GitHub Pages, Netlify, Vercel, Firebase Hosting, or any web server — just upload the file.
Local: open directly from storage (the camera/gallery inputs still work).

To connect it to your trained model in production, host the FastAPI backend (see the backend documentation), enable HTTPS, and set cropguard_api to its URL. CORS is already open on the backend so the browser can call it.

9. Customising it

Change treatment text / add a crop's advice: edit the matching record in the DATA.diseases object (keep the en/tw shape, or just the one language in the single-language files). Class keys must match recommendations.json / classes.json so server predictions resolve to the right record.
Point at a different server: change localStorage.cropguard_api.
Re-theme: all colours are CSS variables at the top of the <style> block (--leaf, --gold, --early/moderate/severe, etc.).

If you add a brand-new crop, you must also add labelled training images and retrain the model (see the Dataset Guide and backend documentation) — the HTML app can display a new class, but only the trained model can detect it.

10. Limitations & browser support

Mode B is an estimate, not the CNN — see §4. For real diagnosis, connect the model.
A photo that isn't a clear leaf is caught by a "not a clear leaf" check and the user is asked to retake it.
Very small lesions, extreme lighting, or a crop/disease not in the model can still be misread — the disclaimer makes this clear.
Browsers: any current Chrome, Firefox, Safari or Edge (mobile or desktop). The camera capture uses the standard file-input capture attribute; on desktop without a camera it falls back to file selection.
Connectivity: Mode A needs internet to reach the server; Mode B needs none.

CropGuard GH — Standalone HTML App Documentation

1. What this is