Haw Yu Hong, Lai Khin Wee, Chuah Joon Huang, Bejo Siti Khairunniza, Husin Nur Azuan, Hum Yan Chai, Yee Por Lip, Tee Clarence Augustine T H, Ye Xin, Wu Xiang
Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ Comput Sci. 2023 Apr 17;9:e1325. doi: 10.7717/peerj-cs.1325. eCollection 2023.
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 . 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, , 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.
油棕是马来西亚一种关键的农业资源。然而，棕榈树疾病，最突出的是基干腐病，每年造成至少2.55亿马来西亚林吉特的经济损失。基干腐病由一种名为 的真菌引起。受感染的树木在感染初期几乎没有症状，但可能会遭受80%的终生产量损失，并且树木可能在两年内死亡。由于可以采取疾病控制措施，因此基干腐病的早期检测至关重要。实验室基干腐病检测方法是有效的，但这些方法存在准确性、生物安全性和成本方面的问题。这篇综述文章包含了自2012年以来在科学网列出的与油棕树疾病、基干腐病、 、遥感和深度学习相关的科学文章。发现约110篇科学文章与上述索引词相关，60篇研究文章与本研究目标相关，因此被纳入这篇综述文章。通过综述发现，深度学习方法的潜在用途很少被探索。一些研究由于数据集的限制而结果不尽人意。然而，基于与其他植物疾病相关的研究，深度学习与数据增强技术相结合显示出巨大潜力，具有显著的检测准确率。因此，应该研究使用深度学习模型和数据增强技术分析油棕遥感数据的可行性。在商业规模上，深度学习与遥感和无人机技术一起使用在基干腐病检测方面显示出巨大潜力。
