Circular Supply Chains: An Internet of Things Application for Rotten Product Detection in Aggregate Food Industry

Candan Ergeldı; Orhan Feyzıoglu

doi:10.55549/epstem.1347745

Conference Paper

Year 2023, Volume: 22, 210 - 216, 01.09.2023

Candan Ergeldı Orhan Feyzıoglu

https://doi.org/10.55549/epstem.1347745

Abstract

References

Angkiriwang R, Pujawan IN, & Santosa B. (2014). Managing uncertainty through supply chain fexibility: reactive vs. proactive approaches. Prod Manuf Res 2, 50–70.
Chakraborty, S., Shamrat, F. J. M., Billah, M. M., Al Jubair, M., Alauddin, M., & Ranjan, R. (2021, June).Implementation of deep learning methods to identify rotten fruits. In 2021 5th international conference on trends in electronics and informatics (ICOEI) (pp. 1207-1212). IEEE.
Das, P., Yadav, J.K.P.S., & Yadav, A.K. (2021). An automated tomato maturity grading system using transfer learning based AlexNet. Ingénierie des Systèmes d’Information, 26(2), 191-200.
Duncan SE, Reinhard R, Williams RC, Ramsey F, Thomason W, Lee K, Dudek N, Mostaghimi S, Colbert E, & Murch R. (2019). Cyberbiosecurity: a new perspective on protecting U.S. Food and Agricultural System. Frontiers in Bioengineering and Biotechnology, 7, Article 63.
Guo T, & Wang Y (2019). Big data application issues in the agricultural modernization of China. Ekoloji 28:36773688Vaibhav S. Narwane, Angappa Gunasekaran, Bhaskar B. Gardas. (2022). Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain, Smart Agricultural Technology, 2, 100035, ISSN 2772-3755

Circular Supply Chains: An Internet of Things Application for Rotten Product Detection in Aggregate Food Industry

Year 2023, Volume: 22, 210 - 216, 01.09.2023

Candan Ergeldı Orhan Feyzıoglu

https://doi.org/10.55549/epstem.1347745

Abstract

Today, the majority of food created is wasted rather than consumed, which has a negative impact on worldwide hunger and the economy. Improvements to aggregate supply chains are at the forefront of the actions needed to meet the nutritional requirements of an expanding population. One of such improvements noted in this research was aggregate food storage. The ESP8266-Microcontroller, along with the DHT11 temperature and humidity sensor and the MQ3 alcohol sensor, is put in the storage area to measure the storage conditions of fruit products on a regular basis. The data gathered is sent to the Internet of Things Application in AWS cloud computing service via the microcontroller and MQTT communication protocol and is stored in both the S3 Bucket and Firehose Kinesis databases using the rules defined in this console. As result, the sensor data stored in the database is examined using AWS-Internet of Things -Analysis and SageMaker. Fruits should be kept at temperatures ranging from 4 to 7 degrees Celsius. When the temperature outside of this range rises, the crops begin to decompose. Accordingly, a rule in the AWS Internet of Things Application is defined to fire with out-of-range measurements, and the AWS Simple Notification Service is triggered to send ambient temperature, humidity, and methanol values to user via SMS and e-mail. A Convolutional Neural Network model was also developed to classify fruits based on their variety and whether they are fresh or rotten. The model was first taught using images of 1693 fresh apples, 1581 fresh bananas, 1466 fresh oranges, 2342 rotten apples, 2224 rotten bananas, and 1595 rotten oranges over 50 epochs. Then, images of 395 fresh apples, 381 fresh bananas, 381 fresh oranges, and 388 rotten apples, 601 rotten bananas, and 530 rotten oranges were evaluated. This CNN Model had a training accuracy of 98.6% and an assessment accuracy of 96.4%.

Keywords

Circular economy , Sustainability , Industry 4.0 , Agrifood supply chain , Internet of things

References

Angkiriwang R, Pujawan IN, & Santosa B. (2014). Managing uncertainty through supply chain fexibility: reactive vs. proactive approaches. Prod Manuf Res 2, 50–70.
Chakraborty, S., Shamrat, F. J. M., Billah, M. M., Al Jubair, M., Alauddin, M., & Ranjan, R. (2021, June).Implementation of deep learning methods to identify rotten fruits. In 2021 5th international conference on trends in electronics and informatics (ICOEI) (pp. 1207-1212). IEEE.
Das, P., Yadav, J.K.P.S., & Yadav, A.K. (2021). An automated tomato maturity grading system using transfer learning based AlexNet. Ingénierie des Systèmes d’Information, 26(2), 191-200.
Duncan SE, Reinhard R, Williams RC, Ramsey F, Thomason W, Lee K, Dudek N, Mostaghimi S, Colbert E, & Murch R. (2019). Cyberbiosecurity: a new perspective on protecting U.S. Food and Agricultural System. Frontiers in Bioengineering and Biotechnology, 7, Article 63.
Guo T, & Wang Y (2019). Big data application issues in the agricultural modernization of China. Ekoloji 28:36773688Vaibhav S. Narwane, Angappa Gunasekaran, Bhaskar B. Gardas. (2022). Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain, Smart Agricultural Technology, 2, 100035, ISSN 2772-3755

There are 5 citations in total.

Details

Primary Language	English
Subjects	Food Engineering
Journal Section	Articles
Authors	Candan Ergeldı Orhan Feyzıoglu
Early Pub Date	August 22, 2023
Publication Date	September 1, 2023
Published in Issue	Year 2023 Volume: 22

Cite

APA	Ergeldı, C., & Feyzıoglu, O. (2023). Circular Supply Chains: An Internet of Things Application for Rotten Product Detection in Aggregate Food Industry. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 22, 210-216. https://doi.org/10.55549/epstem.1347745