Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection,...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1325.pdf |
| _version_ | 1797844129164558336 |
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| author | Yu Hong Haw Khin Wee Lai Joon Huang Chuah Siti Khairunniza Bejo Nur Azuan Husin Yan Chai Hum Por Lip Yee Clarence Augustine T. H. Tee Xin Ye Xiang Wu |
| author_facet | Yu Hong Haw Khin Wee Lai Joon Huang Chuah Siti Khairunniza Bejo Nur Azuan Husin Yan Chai Hum Por Lip Yee Clarence Augustine T. H. Tee Xin Ye Xiang Wu |
| author_sort | Yu Hong Haw |
| collection | DOAJ |
| description | Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease. |
| first_indexed | 2024-04-09T17:17:22Z |
| format | Article |
| id | doaj.art-863e950e1eb64f2c8c7be7a1b3a314e7 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-09T17:17:22Z |
| publishDate | 2023-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-863e950e1eb64f2c8c7be7a1b3a314e72023-04-19T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922023-04-019e132510.7717/peerj-cs.1325Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methodsYu Hong Haw0Khin Wee Lai1Joon Huang Chuah2Siti Khairunniza Bejo3Nur Azuan Husin4Yan Chai Hum5Por Lip Yee6Clarence Augustine T. H. Tee7Xin Ye8Xiang Wu9Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, MalaysiaDepartment of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, MalaysiaDepartment of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, Kajang, Selangor, MalaysiaDepartment of Computer System and Technology, Universiti Malaya, Kuala Lumpur, MalaysiaCollege of Physics and Electrical Information Engineering, Zhejiang Normal University, Zhejiang, ChinaYLZ Eaccessy Information Technology Co., Ltd, Xiamen, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, ChinaOil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.https://peerj.com/articles/cs-1325.pdfBasal stem rotGanoderma boninenseOil palmRemote sensorsDeep learning |
| spellingShingle | Yu Hong Haw Khin Wee Lai Joon Huang Chuah Siti Khairunniza Bejo Nur Azuan Husin Yan Chai Hum Por Lip Yee Clarence Augustine T. H. Tee Xin Ye Xiang Wu Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods PeerJ Computer Science Basal stem rot Ganoderma boninense Oil palm Remote sensors Deep learning |
| title | Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods |
| title_full | Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods |
| title_fullStr | Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods |
| title_full_unstemmed | Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods |
| title_short | Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods |
| title_sort | classification of basal stem rot using deep learning a review of digital data collection and palm disease classification methods |
| topic | Basal stem rot Ganoderma boninense Oil palm Remote sensors Deep learning |
| url | https://peerj.com/articles/cs-1325.pdf |
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