Intelligent Machine for Food Quality and Safety Assessment
Year: 2024 | Volume: 02 | Issue: 01 | Pages:24 - 28
By
1. Vinayak K.*, 2. Anagha M., 3. Kiran T.R., 4. Thara K.V., 5. Binesh Mohan P.
1-4 Student, Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod, Kerala, India
5 Assistant Professor, Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod, Kerala, India
Abstract
This paper presents the development of an intelligent fruit and vegetable classification system aimed at reducing food waste and infections caused by spoiled items. Using artificial intelligence and sensor data, our system classifies items into three categories: ripe, unripe, and rotten, and employs servo motors to sort them into respective baskets. The classification process utilizes real-time temperature, humidity, and image data captured by the camera and processed by the laptop. Additionally, the nutritional information of the ripen fruits are provided after classification. The system employs DC motors for conveyor belt movement, IR proximity sensors for item detection, and DHT11 sensors for environmental monitoring and are controlled by an Arduino Uno R3 microcontroller. Our work demonstrates the potential of such a system in reducing food waste, aiding in diet control, and assisting farmers in agricultural sorting tasks. Overall, this research contributes to the development of intelligent systems for efficient food management, benefiting both consumers and agricultural stakeholders.
Keywords:Artificial Intelligence, Diet control, Food management, and Nutritional information
[This article belongs to International Journal of Industrial and Product Design Engineering IJIPDE]
How to cite this article: Intelligent Machine for Food Quality and Safety Assessment IJIPDE 2024; 02:24 - 28
How to cite this URL: Intelligent Machine for Food Quality and Safety Assessment IJIPDE 2024;{cited 2024-07-02 04:21}; 02:24 - 28. Available from:
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- D. Baswaraj, Dr. Sankirti Shiravale, Dr. Bhagyashree Ashok Tingare, Dr. Rajesh Kedarnath Navandar, Chaitali Ramesh Shewale, and Dr. Sanjeevkumar Angadi. Fruit Detection and Classification application Based on Machine Learning Techniques Framework. IJISAE. 2024; 12(3), 754-7p.
- Zheng Zhou, Umair Zahid, Yaqoob Majeed, Nisha, Sadaf Mustafa, Muhammad Muzzammil Sajjad, Hafiz Danish Butt and Longsheng Fu. Advancement in artificial intelligence for on-farm fruit sorting and transportation. 2023: 11p.
- Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, and Son Vu Truong Dao. A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images with Various Machine Learning Classifiers. IEEE Access. 2022; 10: 10p.
- Miaomiao Zheng, Shanshan Zhang, Yidan Zhang, and Baozhong Hu. Construct Food Safety Traceability System for People’s Health Under the Internet of Things and Big Data. IEEE Access. 2021; 9: 12p.
- Nachiketa Hebbar. Freshness of Food Detection using IoT and Machine Learning. Ic-ETITE. 2020; 978(1): 7281-4142-8p.
- Fu Yuesheng, Song Jian, Xie Fuxiang, Bai Yang, Zheng Xiang, Gao Peng, Wang Zhengtao, and Xie Shengqiao. Circular Fruit and Vegetable Classification Based on Optimized GoogLeNet. IEEE Access. 2021; 9: 11p.
- Bin Yu, Ping Zhan, Ming Lei, Fang Zhou, and Peng Wang. Food Quality Monitoring System Based on Smart Contracts and Evaluation Models. IEEE Access. 2020; 8: 11p.
- Christiena, Dhanushitha H.S, Arsheya N, Ramya C N. Survey on Food Quality Monitoring System. 2020; 7(12): 1957-11p.
- Keshavamurthy, Mariyam Steffi J, Meghamala M. Automatized Food Quality Detection and Processing System Using Neural Networks. RTEICT-2019. 2019; 1442-4p.
- Naveed Shahzad, Usman Khalid, Atif Iqbal, Meezan-Ur-Rahman. e-Fresh-A Device to Detect Food Freshness. 2018; 8(3): 4p.
