Machine learning approach for the classification of corn seed using hybrid features
<p dir="ltr">Seed purity is an important indicator of crop seed quality. On the other side, corn is an important crop of the modern agricultural industry with more than 40% grain Worldwide production. The purpose of this study was to examine the feasibility of a machine learning (ML)...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2020
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513505215905792 |
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| author | Aqib Ali (19680145) |
| author2 | Salman Qadri (8351391) Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (16855434) Samreen Naeem (19680148) Sidra Rafique (19680151) Farrukh Jamal (8605026) Christophe Chesneau (8605029) Sania Anam (19680154) |
| author2_role | author author author author author author author author |
| author_facet | Aqib Ali (19680145) Salman Qadri (8351391) Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (16855434) Samreen Naeem (19680148) Sidra Rafique (19680151) Farrukh Jamal (8605026) Christophe Chesneau (8605029) Sania Anam (19680154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aqib Ali (19680145) Salman Qadri (8351391) Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (16855434) Samreen Naeem (19680148) Sidra Rafique (19680151) Farrukh Jamal (8605026) Christophe Chesneau (8605029) Sania Anam (19680154) |
| dc.date.none.fl_str_mv | 2020-06-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1080/10942912.2020.1778724 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning_approach_for_the_classification_of_corn_seed_using_hybrid_features/27037291 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Crop and pasture production Information and computing sciences Machine learning Corn seeds classification correlation-based feature selection machine learning multilayer perceptron |
| dc.title.none.fl_str_mv | Machine learning approach for the classification of corn seed using hybrid features |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Seed purity is an important indicator of crop seed quality. On the other side, corn is an important crop of the modern agricultural industry with more than 40% grain Worldwide production. The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds. The seed digital images (DI) of six corn varieties were Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, and ICI 339. This was achieved through a digital camera in a natural environment without a complicated laboratory system. The acquired DI dataset converted to a hybrid feature dataset, which is the combination of histogram, texture, and spectral features. For each corn seed image, a total of fifty-five hybrid-features was acquired on every non-overlapping region of interest (ROI), sizes (75 × 75), (100 × 100), (125 × 125) and (150 × 150). The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. To build the classification models, Random forest (RF), BayesNet (BN), LogitBoost (LB), and Multilayer Perceptron (MLP) were employed using optimized multi-feature using (10-fold) cross-validation approach. A comparative analysis of four ML classifiers, the MLP performed outstanding classification accuracy (98.93%), on ROIs size (150 × 150). The accuracy values by MLP on six corn seed verities named Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, ICI- 339 was 99.8%, 97%, 98.5%, 98.6%, 99.9%, and 99.4%, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Food Properties<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/10942912.2020.1778724" target="_blank">https://dx.doi.org/10.1080/10942912.2020.1778724</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_54bfbde6babcc33a2256529164499910 |
| identifier_str_mv | 10.1080/10942912.2020.1778724 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27037291 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning approach for the classification of corn seed using hybrid featuresAqib Ali (19680145)Salman Qadri (8351391)Wali Khan Mashwani (9449980)Samir Brahim Belhaouari (16855434)Samreen Naeem (19680148)Sidra Rafique (19680151)Farrukh Jamal (8605026)Christophe Chesneau (8605029)Sania Anam (19680154)Agricultural, veterinary and food sciencesCrop and pasture productionInformation and computing sciencesMachine learningCorn seedsclassificationcorrelation-based feature selectionmachine learningmultilayer perceptron<p dir="ltr">Seed purity is an important indicator of crop seed quality. On the other side, corn is an important crop of the modern agricultural industry with more than 40% grain Worldwide production. The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds. The seed digital images (DI) of six corn varieties were Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, and ICI 339. This was achieved through a digital camera in a natural environment without a complicated laboratory system. The acquired DI dataset converted to a hybrid feature dataset, which is the combination of histogram, texture, and spectral features. For each corn seed image, a total of fifty-five hybrid-features was acquired on every non-overlapping region of interest (ROI), sizes (75 × 75), (100 × 100), (125 × 125) and (150 × 150). The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. To build the classification models, Random forest (RF), BayesNet (BN), LogitBoost (LB), and Multilayer Perceptron (MLP) were employed using optimized multi-feature using (10-fold) cross-validation approach. A comparative analysis of four ML classifiers, the MLP performed outstanding classification accuracy (98.93%), on ROIs size (150 × 150). The accuracy values by MLP on six corn seed verities named Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, ICI- 339 was 99.8%, 97%, 98.5%, 98.6%, 99.9%, and 99.4%, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Food Properties<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/10942912.2020.1778724" target="_blank">https://dx.doi.org/10.1080/10942912.2020.1778724</a></p>2020-06-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1080/10942912.2020.1778724https://figshare.com/articles/journal_contribution/Machine_learning_approach_for_the_classification_of_corn_seed_using_hybrid_features/27037291CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270372912020-06-28T09:00:00Z |
| spellingShingle | Machine learning approach for the classification of corn seed using hybrid features Aqib Ali (19680145) Agricultural, veterinary and food sciences Crop and pasture production Information and computing sciences Machine learning Corn seeds classification correlation-based feature selection machine learning multilayer perceptron |
| status_str | publishedVersion |
| title | Machine learning approach for the classification of corn seed using hybrid features |
| title_full | Machine learning approach for the classification of corn seed using hybrid features |
| title_fullStr | Machine learning approach for the classification of corn seed using hybrid features |
| title_full_unstemmed | Machine learning approach for the classification of corn seed using hybrid features |
| title_short | Machine learning approach for the classification of corn seed using hybrid features |
| title_sort | Machine learning approach for the classification of corn seed using hybrid features |
| topic | Agricultural, veterinary and food sciences Crop and pasture production Information and computing sciences Machine learning Corn seeds classification correlation-based feature selection machine learning multilayer perceptron |