AI-POWERED FOOD SPOILAGE PREDICTION & SMART WASTE MANAGEMENT SYSTEM

REDUCING GLOBAL FOOD WASTE USING ARTIFICIAL INTELLIGENCE AND IOT

TAGLINE : PREDICT EARLY, SAVE FOOD, IMPROVE SUPPLY CHAINS.

TEAM MEMBERS :

N ANUJA , ABINAYA S, HARSHINI R

PROBLEM STATEMENT

• Food spoilage is a major issue in small retail shops, supermarkets, and food industries .
• Many food products spoil because their storage conditions are not continuously monitored.

Key Problems :

• Spoilage is detected only after food becomes unusable
• Improper temperature and humidity control
• Lack of real-time monitoring systems
• Results in food waste and financial loss

Need for Solution:

A smart system that monitors food conditions and predicts spoilage early to help reduce food waste. 🍎

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IMPACT OF THE PROBLEM

• Financial Loss: Businesses lose money from spoiled unsold food.
• Food Waste: Large amounts of edible food are thrown away.
• Environmental Damage: Food waste releases methane, harming the climate.
• Supply Chain Issues: Poor monitoring causes storage and distribution problems.
• Food Security: Food is wasted while many people lack enough food.

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PROPOSED SOLUTION

• An AI-powered food spoilage prediction system that monitors storage conditions and predicts spoilage early.

Key Features:

• Sensors monitor temperature, humidity, and gas levels
• RFID tags attached to each food packet for identification
• RFID reader scans stored products automatically
• ESP32 collects sensor and RFID data
• AI predicts food freshness and spoilage risk
• Alerts sent to shop owner

Scalability:

• Small shops: Low-cost sensor monitoring
• Food industries: Advanced AI cameras and dashboards 📊

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HARDWARE COMPONENTS

ESP32 / Arduino:

Microcontroller used to collect and process sensor data.

DHT22 Sensor:

Measures temperature and humidity in the storage area.

MQ-135 Gas Sensor:

Detects spoilage gases released by food.

pH Sensor (Optional):

Used to monitor liquid foods like milk.

RIFD Tags:

Attached to food packets for identification

RFID Reader :

Placed near or inside the storage area to scan RFID tags of stored food items and send data to ESP32

LED Indicator / OLED Display:

Shows the freshness status of food.

HOW THE SYSTEM WORK IN SMALL SHOP

• System Working:
• RFID tags are attached to food packets.
• RFID reader scans products placed in the storage rack.
• Sensors monitor temperature, humidity, and spoilage gases from stored food.
• The ESP32 / Arduino collects the sensor data.
• AI analyses the data to calculate freshness level.
• If spoilage risk is detected, the system sends alerts to the shop owner.
• Action Taken:

Sell products quickly Apply discounts Adjust storage conditions

• Benefit :

Helps small shop owners reduce food waste and financial loss

HOW THE SYSTEM WORKS IN FOOD INDUSTRIES

Working Process:

• Sensors monitor storage areas and warehouses
• AI cameras detect spoilage signs (color change, mold)
• Data is sent to a central dashboard
• AI predicts freshness and spoilage time

Action Taken:

• Prioritize product shipment
• Adjust storage temperature
• Convert food to processed products

Benefit:�Prevents large-scale food spoilage and improves supply chain management. 📦🍎

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System Simulation & Working Demonstration

This simulation demonstrates the working of the AI-Based Food Spoilage Monitoring System. Food packets are identified using RFID tags, while sensors monitor temperature, humidity, and gas levels in the storage area. The collected data is processed by the ESP32 microcontroller and analyzed using an AI model to predict food freshness. Based on the analysis, the system classifies food as Fresh, Spoiling Soon, or Spoiled. If spoilage risk is detected, the system generates an alert, helping businesses take quick action and reduce food waste.

Prototype Implementation

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• ESP32 / Arduino acts as the main controller of the system.

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• RFID tags are attached to food packets for unique identification.

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• RFID reader (RC522) scans the stored products automatically.

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• DHT22 and MQ135 sensors monitor temperature, humidity, and spoilage gases

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• The microcontroller collects sensor data and RFID information.

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• AI model analyses the data to predict food freshness and spoilage.

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AI-Based Spoilage Prediction Model

• Input Data:�• Temperature�• Humidity�• Gas concentration�• Food images
• Model Types:�• Random Forest (environmental prediction)�• CNN (image freshness detection)
• Output:�• Fresh�• Spoiling soon�• Spoiled
• Expected Accuracy:�85–92%

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System Architecture

RFID &Sensor:�• Temperature�• Humidity�• Gas levels

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Processing:�• Microcontroller processes data�• Data sent to cloud server

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AI analysis:�• Machine learning model predicts spoilage

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Output:�• Dashboard display�• Mobile notifications

Impact & Benefits

• Benefits:

• Reduces food waste by 30–40%� • Saves money for businesses� • Improves food safety� • Reduces landfill waste

• Target Users:

• Restaurants� • Supermarkets� • Food warehouses� • Households

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Market Opportunity

• Target industries:
• • Food retail�• Restaurants�• Supply chains�• Smart homes
• Global food waste market is rapidly growing.
• Potential customers:
• • Supermarkets�• Cold storage facilities�• Food distributors

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UNIQUE INNOVATION

• Combines RFID tracking + IoT sensors + AI prediction
• Automatically identifies which food packet is spoiling
• Enables real-time product monitoring in storage areas
• It uses an ESP32 microcontroller for real time monitoring and alert generation
• Low-cost smart system for shops and food industries

Implementation Roadmap

• Phase 1 – Prototype�IoT sensors + ESP32 data collection
• Phase 2 – AI Development�Train Random Forest & CNN models
• Phase 3 – Integration�Connect sensors, cloud, and AI system
• Phase 4 – Deployment�Launch in restaurants, supermarkets, and warehouses

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Business Model & Deployment

• Target Customers�• Supermarkets�• Restaurants�• Food warehouses
• Revenue Model�• IoT monitoring hardware�• AI subscription service
• Deployment Areas�• Retail stores�• Food supply chains
• Impact�• Reduce food waste by 30–40%�• Lower business losses�• Promote sustainability 🌱

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Future Scope

Future Improvements:

• Mobile monitoring application�• AI camera for automatic food detection�• Blockchain food traceability�• Smart refrigerator integration

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• Why Our Solution is Unique

• Combines AI + IoT + sustainability�• Real-time spoilage prediction�• Scalable for global food supply chains�• Reduces economic and environmental losses

CONCLUSION

• The AI-Based Food Spoilage Monitoring System detects early food spoilage using sensors, a camera, and an ESP32 device.

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• It monitors temperature, humidity, gas levels, and visual changes in stored food.

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• The system analyzes the data and sends real-time alerts when spoilage risk is detected.

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• This helps reduce food waste, improve food safety, and support better storage management in homes, shops, and food industries. 🍎📡

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