AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango
Abstract This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of man...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2021-05-01
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| Series: | SN Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-021-04657-7 |
| _version_ | 1819137708169101312 |
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| author | Jayita Dutta Parijat Deshpande Beena Rai |
| author_facet | Jayita Dutta Parijat Deshpande Beena Rai |
| author_sort | Jayita Dutta |
| collection | DOAJ |
| description | Abstract This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of mangoes as ‘unripe’, ‘early-ripe’, ‘partially-ripe’ and ‘ideally-ripe’. CO2 respiration rate (RRCO2) was further calculated using principle of enzyme kinetics to establish a correlation between RRCO2 and ripeness levels. A Support Vector Regression model was employed to predict the shelf life and ripeness levels of mangoes under different storage conditions, thereby creating an AI based soft-sensor. The developed methodology can be used for other climacteric fruits besides mangoes. This solution can be used by producers and distributors for post-harvest handling of climacteric fruits like mango. It will also aid retailers in taking dynamic decisions with respect to pricing, logistics and storage conditions to be maintained to get the desired ripening rate, thus, contributing to reduction of wastage of fruits and subsequent economic losses. Article highlights Variation in CO2 respiration rate of ‘Kesar’ mangoes over different maturity stages were observed under different supply chain scenarios simulated in lab environment AI models were developed based on respiration rate and ripeness levels for prediction of shelf life of mangoes under different supply chain scenarios. These models once deployed helps all stake holders in fruit supply chain to take dynamic decisions such as repricing, recycling and repurposing based on the predicted shelf life thus minimizing wastage and maximizing profit. |
| first_indexed | 2024-12-22T10:55:10Z |
| format | Article |
| id | doaj.art-610b45765b4340429bb7ca33dd899f5c |
| institution | Directory Open Access Journal |
| issn | 2523-3963 2523-3971 |
| language | English |
| last_indexed | 2024-12-22T10:55:10Z |
| publishDate | 2021-05-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj.art-610b45765b4340429bb7ca33dd899f5c2022-12-21T18:28:39ZengSpringerSN Applied Sciences2523-39632523-39712021-05-01361910.1007/s42452-021-04657-7AI-based soft-sensor for shelf life prediction of ‘Kesar’ mangoJayita Dutta0Parijat Deshpande1Beena Rai2Physical Sciences Research Area, Tata Research Development and Design Centre (TRDDC), TCS Research, TCSPhysical Sciences Research Area, Tata Research Development and Design Centre (TRDDC), TCS Research, TCSPhysical Sciences Research Area, Tata Research Development and Design Centre (TRDDC), TCS Research, TCSAbstract This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of mangoes as ‘unripe’, ‘early-ripe’, ‘partially-ripe’ and ‘ideally-ripe’. CO2 respiration rate (RRCO2) was further calculated using principle of enzyme kinetics to establish a correlation between RRCO2 and ripeness levels. A Support Vector Regression model was employed to predict the shelf life and ripeness levels of mangoes under different storage conditions, thereby creating an AI based soft-sensor. The developed methodology can be used for other climacteric fruits besides mangoes. This solution can be used by producers and distributors for post-harvest handling of climacteric fruits like mango. It will also aid retailers in taking dynamic decisions with respect to pricing, logistics and storage conditions to be maintained to get the desired ripening rate, thus, contributing to reduction of wastage of fruits and subsequent economic losses. Article highlights Variation in CO2 respiration rate of ‘Kesar’ mangoes over different maturity stages were observed under different supply chain scenarios simulated in lab environment AI models were developed based on respiration rate and ripeness levels for prediction of shelf life of mangoes under different supply chain scenarios. These models once deployed helps all stake holders in fruit supply chain to take dynamic decisions such as repricing, recycling and repurposing based on the predicted shelf life thus minimizing wastage and maximizing profit.https://doi.org/10.1007/s42452-021-04657-7Ripening rateShelf lifeRipeness levelRespiration rateKesar mangoCNN |
| spellingShingle | Jayita Dutta Parijat Deshpande Beena Rai AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango SN Applied Sciences Ripening rate Shelf life Ripeness level Respiration rate Kesar mango CNN |
| title | AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango |
| title_full | AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango |
| title_fullStr | AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango |
| title_full_unstemmed | AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango |
| title_short | AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango |
| title_sort | ai based soft sensor for shelf life prediction of kesar mango |
| topic | Ripening rate Shelf life Ripeness level Respiration rate Kesar mango CNN |
| url | https://doi.org/10.1007/s42452-021-04657-7 |
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