Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques
Increasing marketability and waste management of agricultural products require quality assessment. Meanwhile, their marketability is largely affected by their shapes and overall appearance. Deep Learning (DL) has gained traction as a leading tool for computer vision tasks involving image detection a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302300018X |
| _version_ | 1797947053409566720 |
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| author | Rahim Azadnia Saman Fouladi Ahmad Jahanbakhshi |
| author_facet | Rahim Azadnia Saman Fouladi Ahmad Jahanbakhshi |
| author_sort | Rahim Azadnia |
| collection | DOAJ |
| description | Increasing marketability and waste management of agricultural products require quality assessment. Meanwhile, their marketability is largely affected by their shapes and overall appearance. Deep Learning (DL) has gained traction as a leading tool for computer vision tasks involving image detection and classification. This research was conducted in order to achieve better grading, reduce waste and at the same time increase the export and marketing of hawthorn fruit. In this regard, to classify images, 3 categorizes of hawthorn (unripe, ripe, and overripe) were acquired and the images were prepared using a well-designed illumination chamber. A data augmentation method was employed to improve the DL performance. After the pre-processing step, the capabilities of the suggested Inception-V3, ResNet-50, and the proposed DL models based on convolutional neural networks (CNN) used to grade the hawthorn fruit. In comparison with other methods, the Inception-V3 surpassed the overall validation accuracy of 100%, indicating superiority of this network over the other classifiers. Therefore, CNN and image processing techniques can be effective in increasing marketability, controlling waste and improving traditional methods used for grading hawthorn fruit. |
| first_indexed | 2024-04-10T21:20:41Z |
| format | Article |
| id | doaj.art-6c3a26ce52a94de68a71184355c1407d |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-10T21:20:41Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-6c3a26ce52a94de68a71184355c1407d2023-01-20T04:26:02ZengElsevierResults in Engineering2590-12302023-03-0117100891Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniquesRahim Azadnia0Saman Fouladi1Ahmad Jahanbakhshi2Department of Biosystems Engineering, University of Tehran, Karaj, Iran; Corresponding author.Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, IranDepartment of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, IranIncreasing marketability and waste management of agricultural products require quality assessment. Meanwhile, their marketability is largely affected by their shapes and overall appearance. Deep Learning (DL) has gained traction as a leading tool for computer vision tasks involving image detection and classification. This research was conducted in order to achieve better grading, reduce waste and at the same time increase the export and marketing of hawthorn fruit. In this regard, to classify images, 3 categorizes of hawthorn (unripe, ripe, and overripe) were acquired and the images were prepared using a well-designed illumination chamber. A data augmentation method was employed to improve the DL performance. After the pre-processing step, the capabilities of the suggested Inception-V3, ResNet-50, and the proposed DL models based on convolutional neural networks (CNN) used to grade the hawthorn fruit. In comparison with other methods, the Inception-V3 surpassed the overall validation accuracy of 100%, indicating superiority of this network over the other classifiers. Therefore, CNN and image processing techniques can be effective in increasing marketability, controlling waste and improving traditional methods used for grading hawthorn fruit.http://www.sciencedirect.com/science/article/pii/S259012302300018XFruit classificationWaste managementMachine learningConvolutional neural networksData augmentation |
| spellingShingle | Rahim Azadnia Saman Fouladi Ahmad Jahanbakhshi Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques Results in Engineering Fruit classification Waste management Machine learning Convolutional neural networks Data augmentation |
| title | Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| title_full | Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| title_fullStr | Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| title_full_unstemmed | Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| title_short | Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| title_sort | intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques |
| topic | Fruit classification Waste management Machine learning Convolutional neural networks Data augmentation |
| url | http://www.sciencedirect.com/science/article/pii/S259012302300018X |
| work_keys_str_mv | AT rahimazadnia intelligentdetectionandwastecontrolofhawthornfruitbasedonripeninglevelusingmachinevisionsystemanddeeplearningtechniques AT samanfouladi intelligentdetectionandwastecontrolofhawthornfruitbasedonripeninglevelusingmachinevisionsystemanddeeplearningtechniques AT ahmadjahanbakhshi intelligentdetectionandwastecontrolofhawthornfruitbasedonripeninglevelusingmachinevisionsystemanddeeplearningtechniques |