Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

Tan, Mas Ira Syafila Mohd Hilmi; Jamlos, Mohd Faizal; Omar, Ahmad; Kamarudin, Kamarulzaman; Jamlos, Mohd Aminudin

doi:10.1016/j.chemolab.2022.104718

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…NIR spectroscopy detects G. boninense infection in oil palm trees by analyzing spectral reflectance changes around 1450 nm. Accurate differentiation is achieved using chemometric and machine learning techniques, showcasing the potential for non-destructive detection [ 210 ]. The early detection of G. boninense infections in oil palm seedlings was studied using VIS-NIR hyperspectral images.…”

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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Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Xiong,

McCarthy,

Humpal

et al. 2024

Agronomy Journal

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In Australia, the soil‐borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near‐infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA‐SVM) for early‐stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA‐SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non‐inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions.

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Impact of Industry 4.0 Technologies on the Oil Palm Industry: A Literature Review

Akmal,

Ooi,

et al. 2024

Smart Agricultural Technology

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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

Cited by 5 publications

References 47 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

Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Impact of Industry 4.0 Technologies on the Oil Palm Industry: A Literature Review

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