Case Study // agrihive

AgriHive

Multilingual AI farming assistant achieving 92% disease detection accuracy.

Accuracy92%
LanguageMulti
Latency<2s
OfflineEnabled
DEMO_RECORDING

02 // Architecture Execution

Engineered an offline-first mobile application using Flutter. Integrated a quantized TensorFlow Lite model for on-device inference, allowing for crop disease detection without network dependency.

01 // The Problem Context

Farmers in rural India often lack access to immediate, localized agricultural advice, and internet connectivity is highly unreliable in remote areas.

System Stack

TensorFlow LiteFlutterFirebaseGemini API
core_module.ts
import 'package:tflite_flutter/tflite_flutter.dart';

class DiseaseClassifier {
  late Interpreter _interpreter;

  Future<void> loadModel() async {
    _interpreter = await Interpreter.fromAsset('model_unquant.tflite');
    print('Edge AI: Model Loaded');
  }
}
Launch DemoSource Code