The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality
The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dors...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/1/206 |
| _version_ | 1797626344836694016 |
|---|---|
| author | Ewa Ropelewska Kadir Sabanci Muhammet Fatih Aslan |
| author_facet | Ewa Ropelewska Kadir Sabanci Muhammet Fatih Aslan |
| author_sort | Ewa Ropelewska |
| collection | DOAJ |
| description | The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels <i>R</i>, <i>G</i>, <i>B</i>, <i>L</i>, <i>a</i>, <i>b</i>, <i>X</i>, <i>Y</i>, and <i>Z</i>. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels <i>B</i> (KStar and J48 machine learning algorithms), <i>a</i> (KStar and J48), <i>b</i> (KStar), and <i>Y</i> (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels <i>R</i> and <i>G</i> (J48), <i>B</i>, <i>a</i> and <i>b</i> (KStar, JRip, and J48), and <i>X</i> and <i>Y</i> (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel <i>B</i> (KStar) and <i>a</i> (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels <i>a</i> (KStar, JRip, J48), <i>b</i> (J48), and <i>Z</i> (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves. |
| first_indexed | 2024-03-11T10:09:05Z |
| format | Article |
| id | doaj.art-983b9dc6660e43d493741e19338ee02b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:09:05Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-983b9dc6660e43d493741e19338ee02b2023-11-16T14:52:08ZengMDPI AGApplied Sciences2076-34172022-12-0113120610.3390/app13010206The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf QualityEwa Ropelewska0Kadir Sabanci1Muhammet Fatih Aslan2Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, 96-100 Skierniewice, PolandDepartment of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, TurkeyDepartment of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, TurkeyThe objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels <i>R</i>, <i>G</i>, <i>B</i>, <i>L</i>, <i>a</i>, <i>b</i>, <i>X</i>, <i>Y</i>, and <i>Z</i>. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels <i>B</i> (KStar and J48 machine learning algorithms), <i>a</i> (KStar and J48), <i>b</i> (KStar), and <i>Y</i> (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels <i>R</i> and <i>G</i> (J48), <i>B</i>, <i>a</i> and <i>b</i> (KStar, JRip, and J48), and <i>X</i> and <i>Y</i> (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel <i>B</i> (KStar) and <i>a</i> (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels <i>a</i> (KStar, JRip, J48), <i>b</i> (J48), and <i>Z</i> (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves.https://www.mdpi.com/2076-3417/13/1/206fresh mint leavesdried leavesimage texturesclassificationmachine learning |
| spellingShingle | Ewa Ropelewska Kadir Sabanci Muhammet Fatih Aslan The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality Applied Sciences fresh mint leaves dried leaves image textures classification machine learning |
| title | The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality |
| title_full | The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality |
| title_fullStr | The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality |
| title_full_unstemmed | The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality |
| title_short | The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality |
| title_sort | use of digital color imaging and machine learning for the evaluation of the effects of shade drying and open air sun drying on mint leaf quality |
| topic | fresh mint leaves dried leaves image textures classification machine learning |
| url | https://www.mdpi.com/2076-3417/13/1/206 |
| work_keys_str_mv | AT ewaropelewska theuseofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality AT kadirsabanci theuseofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality AT muhammetfatihaslan theuseofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality AT ewaropelewska useofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality AT kadirsabanci useofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality AT muhammetfatihaslan useofdigitalcolorimagingandmachinelearningfortheevaluationoftheeffectsofshadedryingandopenairsundryingonmintleafquality |