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Offline AI Pest Detection App for Rural Areas | Prototype Demo

This video showcases a working prototype developed for the subject Design and New Product Development, under the guidance of Dr. Chandravadan Goritiyal.

👥 Team Members:

Raj Gupta

Hritik Mhatre

🌾 Project Overview
Farmers in rural areas often face challenges in correctly identifying pests due to limited internet access, lack of expert support, and high costs of existing solutions. Incorrect pest identification can result in crop loss and excessive pesticide usage.

To address this problem, we developed an Offline AI Pest Detection App that uses a small, lightweight pre-trained model to identify pests using camera input—without requiring an internet connection.

📱 What This Prototype Demonstrates

Offline pest detection using AI

Real-time camera-based image classification

Use of a lightweight CNN model for low-resource environments

Stable detection using multi-frame prediction locking

Feasibility testing using cockroach detection as a sample pest

🛠 How the Prototype Was Built

Pest image samples were collected and organized

The model was trained using Google Teachable Machine

The trained model was exported in TensorFlow / Keras format

The model was integrated into a Python-based application

Live testing was performed using camera input

⚙ Technology Stack

Python

Google Teachable Machine

TensorFlow

Keras

OpenCV

🚀 Future Scope

Multi-pest detection (aphids, beetles, locusts, etc.)

Regional language support

Mobile app deployment

Integration with agri-extension and government services

🌱 Conclusion
This prototype demonstrates how small and efficient AI models can be used to build practical, low-cost, and scalable solutions for rural agriculture, supporting better decision-making and sustainable farming practices.

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