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Detection of Freshness of the Fruits using Machine Learning Techniques

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dc.contributor.author Jayasinghe, P. K. S. C.
dc.contributor.author Sammani, S.
dc.date.accessioned 2023-01-11T10:53:09Z
dc.date.available 2023-01-11T10:53:09Z
dc.date.issued 2022-06-30
dc.identifier.citation Sri Lankan Journal of Technology (SLJoT), 3(1); pp.8-17. en_US
dc.identifier.issn 27736970
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/6413
dc.description.abstract Survival period of the fruits after harvest is relatively short. The main objective of this research is to measure the freshness of fruits by observing their CO2 release, water vapor release, and O2 absorption after harvesting for the papaya and watermelon. They were categorized into the three groups (500g-1kg, 1kg-1.5kg, 1.5kg- 2kg) and tested in 4 selected days including the harvested day, three days after harvest, a week after, and two weeks after to observe the changes in these three factors (CO2, O2, and humidity). A CO2 sensor, an O2 sensor, and a humidity sensor was set up to detect the changes. The collected data was used to train the machine learning model (Keras Sequential Model). After entering the type of the fruit, weight, the difference of oxygen, and water vapor concentration after 45 minutes, as inputs for the model, the model will predict the freshness of the fruit as a percentage. The Accuracy of the developed model was considered to be 0.989. The results of the analysis implied that the rate of O2 absorption gradually increases after harvesting and the water vapor release gradually decreases. It is suggested to use higher sensitivity sensors to obtain accurate results. en_US
dc.language.iso en_US en_US
dc.publisher Faculty of Technology, South Eastern University of Sri Lanka, Sri Lanka en_US
dc.subject Freshness en_US
dc.subject Fruits en_US
dc.subject Machine learning en_US
dc.subject Sensors en_US
dc.title Detection of Freshness of the Fruits using Machine Learning Techniques en_US
dc.type Article en_US


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