a. Name of University/Institution Sir Syed CASE Institute of technology
b. Campus Islamabad c. Title of Project Intelligent Fruit Quality Monitoring (project proposal to be attached) d. Discipline/ Group Electrical Engineering e. Name of student (s) (1) Hira Ambreen (2) Seerat Muskan (3) (4) (5) f. Name of Supervisor/Faculty Members Supervisor: Dr. Abdul Khaliq Co-Supervisor: Dr. Muhammad Imran
g. Salient features/ Abstract of Project Our project, 'Intelligent Fruit Quality
(Demonstrating Industrial/ Societal Monitoring,' presents a pioneering approach to problems) revolutionizing the assessment of fruit quality. Combining the power of multi-sensor fusion, artificial intelligence (AI), and image processing, our system offers an unprecedented level of precision in monitoring the ripeness and overall quality of fruits. Through rigorous testing and collaboration with experts, we've crafted a smart harvest intelligence system that not only addresses the shortcomings of traditional methods but also propels fruit quality assurance into the future. Join us on this journey as we redefine the standards of fruit quality monitoring with cutting-edge technology and intelligent insights. h. Project Outcome The 'Intelligent Fruit Quality Monitoring' project envisions a transformative outcome in fruit quality assessment. By harnessing multi- sensor fusion, artificial intelligence, and image processing, our system aims to deliver unparalleled precision in evaluating fruit quality. Anticipated benefits include heightened efficiency through comprehensive assessments, smart harvest decisions, technological advancement in the industry, and a notable contribution to global food security. This project represents a paradigm shift in fruit quality monitoring, promising tangible results that align with sustainability and technological innovation.
i. Project Estimate 50,000 Rupees approx.
j. Has the project been included in any No. National or International Competition (Yes/No) (If Yes, then details of getting any prize or certificate)
k. Innovation/ development/ To further advance our 'Intelligent Fruit
improvement in the existing Quality Monitoring' project, we envision procedure implemented through the implementing real-time monitoring Project enhancements for the entire supply chain, introducing blockchain technology for transparent traceability, exploring additional advanced sensors, developing a user-friendly mobile application, customizing AI models for different fruit varieties, continuously optimizing machine learning models with diverse datasets, integrating user feedback mechanisms, tailoring the system for global adaptability, researching energy-efficient IoT modules, and establishing collaborations with agricultural research institutions. This comprehensive approach aims not only to refine and expand the current system but also to position it as a dynamic and evolving solution that remains at the forefront of technological innovation in fruit quality monitoring.
Signature with
Stamp Name of HoD:
PROJECT PROPOSAL Intelligent Fruit Quality Monitoring Title:
Intelligent Fruit Quality Monitoring: Advanced Fruit Ripeness Monitoring System
using Multi-Sensor Fusion
Introduction: In the dynamic landscape of the food industry, ensuring the quality of fruits is paramount. This project aims to develop an advanced fruit ripeness monitoring system that employs multi-sensor fusion technology to precisely determine the ripeness of fruits. By integrating cutting-edge sensors and analytical methods, this system will revolutionize the assessment of fruit quality, providing real-time and accurate information to stakeholders in the industry. Objectives: Develop a robust and efficient system for fruit ripeness monitoring. Utilize multi-sensor fusion technology to enhance the accuracy of ripeness assessments. Implement a user-friendly interface for easy interpretation of ripeness data. Explore machine learning algorithms for predictive analysis of fruit ripeness trends.
Proposed Hardware Components:
NIR Spectral Sensor Gas Sensor (MQ-3 Gas Sensor, MQ-6 Gas Sensor,MQ-8 Gas Sensor, MQ-135 Gas Sensor, Microcontroller Unit (e.g., Arduino Nano) Small DC Fan IoT Connectivity Module (e.g., ESP32)
Proposed Software Components:
Arduino IDE CoolTerm Google Colab CorelDRAW Software
Methodology: Collect data using NIR spectrometer, gas sensor array. Develop algorithms for multi-sensor fusion to enhance ripeness accuracy. Implement machine learning models for predictive ripeness analysis. Integrate data processing and analysis into a user-friendly web interface.
Expected Outcomes: Highly accurate and real-time fruit ripeness assessments. User-friendly interface for easy interpretation of ripeness data. Predictive analysis capabilities for anticipating ripeness trends. Improved decision-making in the food industry for timely harvesting and distribution. Result: Our project successfully obtain objectives:
NO Apple Detected Under Ripe Apple Detected
Full Ripe Apple Detected
Over Ripe Apple Detected
Discussion: The system effectively identifies underripe, ripe, and overripe apples based on sensor data (gas emissions and spectral signatures). This information aids in quality control, supply chain management, and reducing waste. Challenges The system sometimes fails to detect apples. This issue needs investigation to improve accuracy and reliability. Overall The system is valuable for the apple industry as it helps maintain quality, reduce losses, and optimize processes. Ongoing improvements are necessary to enhance its performance.
Seerat Muskan & Hira Ambreen
SS CASE IT seeratmuskan2001@gmail.com , hiraambreen2002@gmail.com
