Classifying the Vermicompost Production Stages Using Thermal Camera Data
The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10345537/ |
| _version_ | 1797376335637643264 |
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| author | Asmaa Mohamed Ahmed A. Akl M. M. Badr Khalil Al Ruqeishi Ahmad Salah Amr M. Abdelatif |
| author_facet | Asmaa Mohamed Ahmed A. Akl M. M. Badr Khalil Al Ruqeishi Ahmad Salah Amr M. Abdelatif |
| author_sort | Asmaa Mohamed |
| collection | DOAJ |
| description | The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%. |
| first_indexed | 2024-03-08T19:37:04Z |
| format | Article |
| id | doaj.art-272f68786ad24ffc98113e865ad01009 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-272f68786ad24ffc98113e865ad010092023-12-26T00:03:24ZengIEEEIEEE Access2169-35362023-01-011113967813968710.1109/ACCESS.2023.333988410345537Classifying the Vermicompost Production Stages Using Thermal Camera DataAsmaa Mohamed0https://orcid.org/0000-0002-9531-1834Ahmed A. Akl1M. M. Badr2Khalil Al Ruqeishi3https://orcid.org/0000-0002-5962-303XAhmad Salah4https://orcid.org/0000-0003-3433-7640Amr M. Abdelatif5https://orcid.org/0000-0003-1153-8538Faculty of Computers and Informatics, Zagazig University, Zagazig, EgyptFaculty of Computers and Information, Damanhour University, Damanhour, EgyptFaculty of Agriculture, Zagazig University, Zagazig, EgyptDepartment of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa, Nizwa, OmanFaculty of Computers and Informatics, Zagazig University, Zagazig, EgyptFaculty of Computers and Informatics, Zagazig University, Zagazig, EgyptThe procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.https://ieeexplore.ieee.org/document/10345537/Classificationdeep learningmachine learningResNetSENetsensor array |
| spellingShingle | Asmaa Mohamed Ahmed A. Akl M. M. Badr Khalil Al Ruqeishi Ahmad Salah Amr M. Abdelatif Classifying the Vermicompost Production Stages Using Thermal Camera Data IEEE Access Classification deep learning machine learning ResNet SENet sensor array |
| title | Classifying the Vermicompost Production Stages Using Thermal Camera Data |
| title_full | Classifying the Vermicompost Production Stages Using Thermal Camera Data |
| title_fullStr | Classifying the Vermicompost Production Stages Using Thermal Camera Data |
| title_full_unstemmed | Classifying the Vermicompost Production Stages Using Thermal Camera Data |
| title_short | Classifying the Vermicompost Production Stages Using Thermal Camera Data |
| title_sort | classifying the vermicompost production stages using thermal camera data |
| topic | Classification deep learning machine learning ResNet SENet sensor array |
| url | https://ieeexplore.ieee.org/document/10345537/ |
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