I Built an Offline AI Crop Disease Identifier for Smart India Hackathon

Source: Dev.to

🌐 Live demo: https://dev48v.infy.uk/solve/day3-crop-disease.html

Day 3 of SolveFromZero — pick a real hackathon brief, ship a working solution. Today’s problem is a perennial Smart India Hackathon: 35% of Indian crop losses come from undiagnosed disease, and the village ag-extension officer can’t visit every farm.

The fix: a phone app where the farmer photographs a sick leaf, AI returns the disease + a remedy in Hindi or Marathi. Runs fully offline on a ₹6,000 Android phone.

The hard constraints

Hardware ₹6,000 Android phone, 2 GB RAM

Network Patchy 2G in rural areas

Model size Must fit in { const img = new Image(); img.src = URL.createObjectURL(e.target.files[0]); await img.decode();

const tensor = tf.browser.fromPixels(img) .resizeBilinear([224, 224]) .toFloat().div(255).expandDims();

const probs = await model.predict(tensor).data(); const top = probs.indexOf(Math.max(…probs));

console.log(CLASSES[top], probs[top]); };

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  Stage 5 — Remedy lookup + Hindi voice

const REMEDIES = { “tomato_late_blight”: { treatment: “Spray 2g/L Mancozeb every 7 days for 3 weeks.”, hindi: “मैनकोज़ेब 2 ग्राम/लीटर पानी में मिलाकर 7 दिन में छिड़काव करें।” }, // … 38 entries };

const r = REMEDIES[CLASSES[top]];

const utter = new SpeechSynthesisUtterance(r.hindi); utter.lang = “hi-IN”; speechSynthesis.speak(utter);

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Many farmers can't read. Voice output is the difference between "interesting demo" and "actually deployable tool."

  
  
  Offline-first PWA (the critical detail)

Rural villages have spotty 2G. App must work without network after first install:

// service-worker.js self.addEventListener(“install”, e => { e.waitUntil( caches.open(“crop-v1”).then(cache => cache.addAll([ ”/”, “/index.html”, “/app.js”, “/model/model.json”, “/model/weights.bin” // 3.5 MB ]) ) ); });

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After the first visit, the app + model are cached. Works in airplane mode. This is what makes the app **actually deployable**.

  
  
  Try it now

3-tier page with simulated 4-leaf classifier + 9-step understanding:

**[https://dev48v.infy.uk/solve/day3-crop-disease.html](https://dev48v.infy.uk/solve/day3-crop-disease.html)**

  
  
  What this unlocks

Same pipeline (small CNN + lookup table + voice) works for:

Problem
Model swap

Cattle disease ID
PlantDoc-style cattle dataset

Weed identification
DeepWeeds dataset

Soil-color → nutrient deficiency
Custom dataset + simple classifier

Insect pest identification
IP102 dataset

The CNN + offline PWA + multilingual voice = the template for **any** rural-village AI deployment.

  
  
  What's next in SolveFromZero

Day 4: **GitHub repo health scorer** (Devpost). Paste a GitHub URL → get a health score based on stars, recent commits, issue/PR ratios.

🌐 All problems: [https://dev48v.infy.uk/solvefromzero.php](https://dev48v.infy.uk/solvefromzero.php)