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Kumar TA, Rajmohan R, Adeola Ajagbe S, Gaber T, Zeng XJ, Masmoudi F. A novel CNN gap layer for growth prediction of palm tree plantlings. PLoS One 2023; 18:e0289963. [PMID: 37566602 PMCID: PMC10420369 DOI: 10.1371/journal.pone.0289963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.
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Affiliation(s)
- T. Ananth Kumar
- Computer Science and Engineering, IFET College of Engineering, Valavanur, Viluppuram, India
| | - R. Rajmohan
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Sunday Adeola Ajagbe
- Department of Computer & Industrial Production Engineering, First Technical University Ibadan, Ibadan, Nigeria
| | - Tarek Gaber
- Computer Science & Software Engineering, University of Salford, Manchester, United Kingdom
- Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
| | - Xiao-Jun Zeng
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Fatma Masmoudi
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
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Haw YH, Lai KW, Chuah JH, Bejo SK, Husin NA, Hum YC, Yee PL, Tee CATH, Ye X, Wu X. Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods. PeerJ Comput Sci 2023; 9:e1325. [PMID: 37346512 PMCID: PMC10280561 DOI: 10.7717/peerj-cs.1325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/13/2023] [Indexed: 06/23/2023]
Abstract
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.
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Affiliation(s)
- Yu Hong Haw
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siti Khairunniza Bejo
- Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nur Azuan Husin
- Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, Kajang, Selangor, Malaysia
| | - Por Lip Yee
- Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Xin Ye
- YLZ Eaccessy Information Technology Co., Ltd, Xiamen, China
| | - Xiang Wu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
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