Application Specific Electronic Nose (ASEN) for Ganoderma boninense detection using artificial neural network

Abdullah, Azizi; Shakaff, Ali Yeon Md; Zakaria, A.; Saad, Fathinul Syahir Ahmad; Shukor, Shazmin Aniza Abdul; Mat, Abdul Rahman

doi:10.1109/iced.2014.7015788

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…The ASEN utilizes specific sensor arrays and Artificial Neural Network (ANN) classification models for effective detection. Initial results show promising potential for this low-cost and non-destructive method in disease detection and plant disease monitoring [ 141 ].…”

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

View full text Add to dashboard Cite

show abstract

“…The classification was 100% successful when using the multilayer perceptron and probabilistic neutral network algorithms, while a 97.5% success rate was achieved when using the radial basis functions (RBF) algorithm. All three of these methods are ANN methods but use different types of supervision [ 78 ]. A back-propagation feed-forward artificial neutral network (BP-ANN) has also been employed to differentiate different apple cultivars, and showed a satisfactory accuracy of 87% [ 79 ].…”

Section: Electronic Nose Detecting Technologymentioning

confidence: 99%

Plant Pest Detection Using an Artificial Nose System: A Review

Cui

Ling

Zhu

et al. 2018

Sensors

157

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This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provide functional information about the plant’s growth, defense, and health status, allow for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms. Compared with traditional gas chromatography–mass spectrometry (GC-MS) techniques, electronic noses are noninvasive and can be a rapid, cost-effective option for several applications. However, using electronic noses for plant pest diagnosis is still in its early stages, and there are challenges regarding sensor performance, sampling and detection in open areas, and scaling up measurements. This review paper introduces each element of electronic nose systems, especially commonly used sensors and pattern recognition methods, along with their advantages and limitations. It includes a comprehensive comparison and summary of applications, possible challenges, and potential improvements of electronic nose systems for different plant pest diagnoses.

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Detection and Management of Basal Stem Rot of Oil Palm: Classical to Modern Approaches

Keerthana¹,

Lakshmi²,

Aditya³

et al. 2023

Detection, Diagnosis and Management of Soil-Borne Phytopathogens

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Application Specific Electronic Nose (ASEN) for Ganoderma boninense detection using artificial neural network

Cited by 8 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

Plant Pest Detection Using an Artificial Nose System: A Review

Detection and Management of Basal Stem Rot of Oil Palm: Classical to Modern Approaches

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