Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models
Timely monitoring of fruit weight is a paramount concern for the improvement of productivity and quality in strawberry cultivation. Therefore, the present study was conducted to introduce a simple non-destructive technique with machine learning models in measuring fruit weight of strawberries. Nine...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/10/2487 |
| _version_ | 1827652296838742016 |
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| author | Jayanta Kumar Basak Bhola Paudel Na Eun Kim Nibas Chandra Deb Bolappa Gamage Kaushalya Madhavi Hyeon Tae Kim |
| author_facet | Jayanta Kumar Basak Bhola Paudel Na Eun Kim Nibas Chandra Deb Bolappa Gamage Kaushalya Madhavi Hyeon Tae Kim |
| author_sort | Jayanta Kumar Basak |
| collection | DOAJ |
| description | Timely monitoring of fruit weight is a paramount concern for the improvement of productivity and quality in strawberry cultivation. Therefore, the present study was conducted to introduce a simple non-destructive technique with machine learning models in measuring fruit weight of strawberries. Nine hundred samples from three strawberry cultivars, i.e., Seolhyang, Maehyang, and Santa (300 samples in each cultivar), in six different ripening stages were randomly collected for determining length, diameter, and weight of each fruit. Pixel numbers of each captured fruit’s image were calculated using image processing techniques. A simple linear-based regression (LR) and a nonlinear regression, i.e., support vector regression (SVR) models were developed by using pixel numbers as input parameter in modeling fruit weight. Findings of the study showed that the LR model performed slightly better than the SVR model in estimating fruit weight. The LR model could explain the relationship between the pixel numbers and fruit weight with a maximum of 96.3% and 89.6% in the training and the testing stages, respectively. This new method is promising non-destructive, time-saving, and cost-effective for regularly monitoring fruit weight. Hereafter, more strawberry samples from various cultivars might need to be examined for the improvement of model performance in estimating fruit weight. |
| first_indexed | 2024-03-09T20:53:31Z |
| format | Article |
| id | doaj.art-5fb7c92b4e434fbdb3de13c5a6ad8420 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:53:31Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-5fb7c92b4e434fbdb3de13c5a6ad84202023-11-23T22:28:05ZengMDPI AGAgronomy2073-43952022-10-011210248710.3390/agronomy12102487Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning ModelsJayanta Kumar Basak0Bhola Paudel1Na Eun Kim2Nibas Chandra Deb3Bolappa Gamage Kaushalya Madhavi4Hyeon Tae Kim5Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaDepartment of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, KoreaTimely monitoring of fruit weight is a paramount concern for the improvement of productivity and quality in strawberry cultivation. Therefore, the present study was conducted to introduce a simple non-destructive technique with machine learning models in measuring fruit weight of strawberries. Nine hundred samples from three strawberry cultivars, i.e., Seolhyang, Maehyang, and Santa (300 samples in each cultivar), in six different ripening stages were randomly collected for determining length, diameter, and weight of each fruit. Pixel numbers of each captured fruit’s image were calculated using image processing techniques. A simple linear-based regression (LR) and a nonlinear regression, i.e., support vector regression (SVR) models were developed by using pixel numbers as input parameter in modeling fruit weight. Findings of the study showed that the LR model performed slightly better than the SVR model in estimating fruit weight. The LR model could explain the relationship between the pixel numbers and fruit weight with a maximum of 96.3% and 89.6% in the training and the testing stages, respectively. This new method is promising non-destructive, time-saving, and cost-effective for regularly monitoring fruit weight. Hereafter, more strawberry samples from various cultivars might need to be examined for the improvement of model performance in estimating fruit weight.https://www.mdpi.com/2073-4395/12/10/2487fruit weightimage processing techniquelinear regressionnon-destructive methodspixel numbersstrawberry |
| spellingShingle | Jayanta Kumar Basak Bhola Paudel Na Eun Kim Nibas Chandra Deb Bolappa Gamage Kaushalya Madhavi Hyeon Tae Kim Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models Agronomy fruit weight image processing technique linear regression non-destructive methods pixel numbers strawberry |
| title | Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models |
| title_full | Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models |
| title_fullStr | Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models |
| title_full_unstemmed | Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models |
| title_short | Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models |
| title_sort | non destructive estimation of fruit weight of strawberry using machine learning models |
| topic | fruit weight image processing technique linear regression non-destructive methods pixel numbers strawberry |
| url | https://www.mdpi.com/2073-4395/12/10/2487 |
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