Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging

Kurihara, Junichi; Koo, Voon Chet; Guey, Cheaw Wen; Lee, Yang Ping; Abidin, Haryati

doi:10.3390/rs14030799

Cited by 31 publications

(18 citation statements)

References 36 publications

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“…These changes can be detected through remote sensing in order to monitor the health of plants and identify areas of water stress [32,40,59,60]. One way to detect water stress in plants is by measuring changes in the reflectance of visible and near-infrared light [40,59,61]. In general, stressed plants will have a lower reflectance in the near-infrared region and a higher reflectance in the visible region [45].…”

Section: General Principlesmentioning

confidence: 99%

Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests

Harper

Dell

2023

Remote Sensing

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In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress. This review aims to provide an overview of remote sensing applications for monitoring water stress in forests and reveal the potential of remote sensing and geographic information system applications in monitoring water stress for effective forest resource management. It examines the principles and significance of utilizing remote sensing technologies to detect forest stress caused by water deficit. In addition, by a quantitative assessment of remote sensing applications of studies in refereed publications, the review highlights the overall trends and the value of the widely used approach of utilizing visible and near-infrared reflectance data from satellite imagery, in conjunction with classical vegetation indices. Promising areas for future research include the utilization of more adaptable platforms and higher-resolution spectral data, the development of novel remote sensing indices with enhanced sensitivity to forest water stress, and the implementation of modelling techniques for early detection and prediction of stress.

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Section: General Principlesmentioning

confidence: 99%

Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests

Harper

Dell

2023

Remote Sensing

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“… Kurihara et al (2022) used an UAV with hyperspectral imager and used RF machine learning algorithm to classify oil palm disease into various infection severity category. The hyperspectral imager is a sequential two-dimensional imager (Genesia Corp., Tokyo, Japan).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The team was able to calculate the mean reflectance spectra of each segment of the hyperspectral cube of the scene. The results showed that only some spectral bands in the red-edge region could be used adequately to classify BSR infection levels ( Kurihara et al, 2022 ). This was also explained by Horler, Dockray & Barber (1983) , where the red-edge region were tightly linked to the light absorption by the chlorophylls on the plant cells.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Haw

Lai

Chuah

et al. 2023

PeerJ Computer Science

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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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“…During the past 20 years the technology of unmanned aerial vehicle (UAV) has been applied to a number of studies in various fields, including environment, forestry and ecosystem, and wildfire effect. For agriculture, UAVs have been used to develop the precision agriculture (Akhtman, et al, 2017;Tsouros et al, 2019;Zheng, et al, 2021;Velusamy, et al, 2022) and analyze crop health (Izzuddin et al, 2020;Kurihara et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Height Measurement and Oil Palm Yield Prediction Using Unmanned Aerial Vehicle (Uav) Data to Create Canopy Height Model (Chm)

Kulpanich¹,

WORACHAIRUNGREUNG²,

Waiyasusri³

et al. 2022

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Oil palms are currently in high demand, which tend to increase even higher as a source of alternative energy for humans, especially in Southeast Asian countries. This leads to the study that focuses on the height measurement, using an unmanned aerial vehicle (UAV), and age analysis of oil palm trees planted within the experimental plots in order to predict their yield. The methodology described in the paper provides using Canopy Height Model (CHM) for height measurement and prediction of the oil palm yield by multiple linear regression. The results indicated that the errors caused by overlapping age ranges were found in 3 out of 12 experimental plots. Furthermore, the primary factors influencing the oil palm yield prediction included the age (9 years and above) and canopy density (over 41% of the area), while the secondary factors supporting more accuracy included the total plot area, canopy area, and canopy height, with the coefficient of determination or R-squared at 0.98. In this study, we learned that the aforementioned factors could be concluded from the data collected by an UAV, which reduced the time for measuring the height of each tree manually, resulting in more accurate yield prediction.

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Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging

Cited by 31 publications

References 36 publications

Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests

Application of Remote Sensing in Detecting and Monitoring Water Stress in Forests

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Height Measurement and Oil Palm Yield Prediction Using Unmanned Aerial Vehicle (Uav) Data to Create Canopy Height Model (Chm)

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