CASE STUDY ARCHITECTURE

AI for Social Good Platform

Lightweight edge machine learning models for local welfare

Executive Summary

This initiative focuses on deploying lightweight, offline-first Machine Learning models on low-power devices. We develop crop disease classification, offline OCR for regional documents, and audio-based literacy cues to make digital services accessible to all.

Problem Statement

Advanced AI solutions require high-performance cloud servers and stable internet, which are unavailable in remote villages, excluding them from modern AI benefits.

Proposed Solution

Optimize and deploy quantized ML models directly onto local edge computers (Raspberry Pi/smartphones) for offline classification tasks.

Project Objectives

check_circle Train quantized ML classification models
check_circle Build offline mobile OCR apps
check_circle Quantify crop health indicators
check_circle Expand digital accessibility.

Technology Integration & Infrastructure

TensorFlow Lite, Python, edge computers, mobile application frameworks.

Implementation Methodology

1. Collect localized crop disease and regional document image datasets; 2. Train and quantize neural networks; 3. Build offline mobile applications; 4. Conduct field testing with local volunteers.

Expected Outcomes

Working offline AI tools helping farmers identify crop diseases instantly and enabling digital document reads for non-literate adults.

Impact & Measurable Results

Piloted crop disease classification app with 80 farmers in rural Nagpur, achieving 88% accuracy offline.

Frequently Asked Questions

Does the app require internet?

No. The neural network model is compressed and runs locally inside the Android application, giving results in seconds without network connectivity.

Which crops are supported?

Our current pilot supports cotton and soybean crop disease classification, which are common in the Nagpur region.