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A novel dataset of guava fruit for grading and classification

Machine learning algorithms play a vital role in object detection and recognition. Currently, Machine learning techniques have achieved significant performance in various areas. However, there is still a need for research in the agriculture sector. The fruit harvesting process is carried out by unsk...

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Detalles Bibliográficos
Autores principales: Maitlo, Abdul Khalique, Aziz, Abdul, Raza, Hassnian, Abbas, Neelam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412757/
https://www.ncbi.nlm.nih.gov/pubmed/37577735
http://dx.doi.org/10.1016/j.dib.2023.109462
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author Maitlo, Abdul Khalique
Aziz, Abdul
Raza, Hassnian
Abbas, Neelam
author_facet Maitlo, Abdul Khalique
Aziz, Abdul
Raza, Hassnian
Abbas, Neelam
author_sort Maitlo, Abdul Khalique
collection PubMed
description Machine learning algorithms play a vital role in object detection and recognition. Currently, Machine learning techniques have achieved significant performance in various areas. However, there is still a need for research in the agriculture sector. The fruit harvesting process is carried out by unskilled labour without using modern scientific technologies; resultantly, the accuracy of harvesting is compromised. Moreover, immature fruits were harvested, which caused revenue losses and pretended sustainable growth. Therefore, the classification and grading of fruits are increasingly highlighted amongst the research communities. This article presents a novel dataset for local varieties such as Local Sindhi, Thadhrami and Riyali of guava fruit harvested in the Larkana region of Pakistan. The dataset is a primary instrument for developing an autonomous system using machine learning and deep learning methods. Hence, it has come up with an indigenous and state-of-the-art dataset. The dataset was developed using varieties as mentioned above. The dataset has been classified into three folders; each folder was further divided into three subfolders related to maturity level (i) Green, (ii) Mature Green, and (iii) Ripe. Images have been acquired in a controlled environment. The proposed dataset contains 2,309 total images in jpg format. This dataset will contribute to developing machine learning-based systems for the agricultural sector.
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spelling pubmed-104127572023-08-11 A novel dataset of guava fruit for grading and classification Maitlo, Abdul Khalique Aziz, Abdul Raza, Hassnian Abbas, Neelam Data Brief Data Article Machine learning algorithms play a vital role in object detection and recognition. Currently, Machine learning techniques have achieved significant performance in various areas. However, there is still a need for research in the agriculture sector. The fruit harvesting process is carried out by unskilled labour without using modern scientific technologies; resultantly, the accuracy of harvesting is compromised. Moreover, immature fruits were harvested, which caused revenue losses and pretended sustainable growth. Therefore, the classification and grading of fruits are increasingly highlighted amongst the research communities. This article presents a novel dataset for local varieties such as Local Sindhi, Thadhrami and Riyali of guava fruit harvested in the Larkana region of Pakistan. The dataset is a primary instrument for developing an autonomous system using machine learning and deep learning methods. Hence, it has come up with an indigenous and state-of-the-art dataset. The dataset was developed using varieties as mentioned above. The dataset has been classified into three folders; each folder was further divided into three subfolders related to maturity level (i) Green, (ii) Mature Green, and (iii) Ripe. Images have been acquired in a controlled environment. The proposed dataset contains 2,309 total images in jpg format. This dataset will contribute to developing machine learning-based systems for the agricultural sector. Elsevier 2023-07-28 /pmc/articles/PMC10412757/ /pubmed/37577735 http://dx.doi.org/10.1016/j.dib.2023.109462 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Maitlo, Abdul Khalique
Aziz, Abdul
Raza, Hassnian
Abbas, Neelam
A novel dataset of guava fruit for grading and classification
title A novel dataset of guava fruit for grading and classification
title_full A novel dataset of guava fruit for grading and classification
title_fullStr A novel dataset of guava fruit for grading and classification
title_full_unstemmed A novel dataset of guava fruit for grading and classification
title_short A novel dataset of guava fruit for grading and classification
title_sort novel dataset of guava fruit for grading and classification
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412757/
https://www.ncbi.nlm.nih.gov/pubmed/37577735
http://dx.doi.org/10.1016/j.dib.2023.109462
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