Selection of Soil Features for Detection of Ganoderma Using Rough Set Theory

Zamry, Nurfazrina Mohd; Zainal, Anazida; Rassam, Murad A.; Bakhtiari, Majid; Maarof, Mohd Aizaini

doi:10.1007/978-3-319-17398-6_28

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…WSNs have potential in palm oil plantations for Ganoderma detection. Feature selection using a rough set technique and a genetic algorithm improves efficiency in selecting relevant soil features for data transmission [ 226 ]. Fourier transform infrared spectroscopy (FTIR) shows promise as a detection tool for G. boninense in oil palm trees.…”

Section: Research On Detection Methods For Basal Stem Rotmentioning

confidence: 99%

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

Karunarathna,

Patabendige,

et al. 2024

JoF

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Phytopathogenic Ganoderma species pose a significant threat to global plant health, resulting in estimated annual economic losses exceeding USD (US Dollars) 68 billion in the agriculture and forestry sectors worldwide. To combat this pervasive menace effectively, a comprehensive understanding of the biology, ecology, and plant infection mechanisms of these pathogens is imperative. This comprehensive review critically examines various aspects of Ganoderma spp., including their intricate life cycle, their disease mechanisms, and the multifaceted environmental factors influencing their spread. Recent studies have quantified the economic impact of Ganoderma infections, revealing staggering yield losses ranging from 20% to 80% across various crops. In particular, oil palm plantations suffer devastating losses, with an estimated annual reduction in yield exceeding 50 million metric tons. Moreover, this review elucidates the dynamic interactions between Ganoderma and host plants, delineating the pathogen’s colonization strategies and its elicitation of intricate plant defense responses. This comprehensive analysis underscores the imperative for adopting an integrated approach to Ganoderma disease management. By synergistically harnessing cultural practices, biological control, and chemical treatments and by deploying resistant plant varieties, substantial strides can be made in mitigating Ganoderma infestations. Furthermore, a collaborative effort involving scientists, breeders, and growers is paramount in the development and implementation of sustainable strategies against this pernicious plant pathogen. Through rigorous scientific inquiry and evidence-based practices, we can strive towards safeguarding global plant health and mitigating the dire economic consequences inflicted by Ganoderma infections.

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Section: Research On Detection Methods For Basal Stem Rotmentioning

confidence: 99%

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

Karunarathna,

Patabendige,

et al. 2024

JoF

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“…Nevertheless, it is costly and time-consuming to visually monitor this disease in the field during its early phases. (Liaghat et al 2014;Zamry et al 2015). In the early stage of Ganoderma infection, palms are symptomless making it difficult to diagnose..…”

Section: Introductionmentioning

confidence: 99%

CONVOLUTIONAL SVM NETWORKS FOR DETECTION OF GANODERMA BONINENSE AT EARLY STAGE IN OIL PALM USING UAV AND MULTISPECTRAL PLEIADES IMAGES

Ahmadi

Mansor²,

Araji³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Oil palm performs a considerable role in Malaysia’s economic system as Malaysia is the second-biggest palm oil manufacturer in the world. In oil palm plantations. Basal stem rot (BSR) is a disease caused by Ganoderma boninense that is responsible for a considerable annual losses, particularly in South East Asia. The disease remains an unresolved problem in most production areas due to lack of disease management strategy to detect the infected palms at their early stage. In recent years, advancement in remote sensing platforms and image processing methods have produced remarkable results for the detection of diseases at early stage. In this study, support vector machine (SVM) classifier was performed on UAV and Pleiades imagery to determine the ideal classification model for the early diagnosis of BSR disease in oil palms. The investigation's results showed that UAV provided the most accurate prediction, with a total accuracy of 68.28%, while 64.52% of the early Ganoderma infections could be identified with accuracy levels of 64.07% and 64.49%, respectively. The early Ganoderma infection could be recognized with an overall accuracy of 64.07% and 64.49%, respectively, while the Pleiades had an overall accuracy of 68.28% and 64.52%. Although the categorization accuracy appeared to be only modest at first glance, the quantity of detail offered by the imageries suggested that the accuracies were acceptable.

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Selection of Soil Features for Detection of Ganoderma Using Rough Set Theory

Cited by 2 publications

References 13 publications

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

CONVOLUTIONAL SVM NETWORKS FOR DETECTION OF GANODERMA BONINENSE AT EARLY STAGE IN OIL PALM USING UAV AND MULTISPECTRAL PLEIADES IMAGES

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