Intelligent electronic nose system for basal stem rot disease detection

Markom, Marni Azira; Shakaff, Ali Yeon Md; Adom, Abdul Hamid; Ahmad, Mardiana Idayu; Hidayat, Wahyu; Abdullah, Azizi; Fikri, N. A.

doi:10.1016/j.compag.2009.01.006

Cited by 97 publications

(45 citation statements)

References 8 publications

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“…Studies on fungi-induced VOCs include those on peanut (Arachis hypogaea) upon Sclerotium rolfsii infection (8), on silver birch (Betula pendula) upon Marssonina betulae infection (93), on oil palm (Elaeis guineensis) upon Ganoderma boninense infection (57), and on willow (Salix spp.) upon Melampsora epitea infection (86).…”

Section: Emissions As a Results Of Infectious Plant Diseasesmentioning

confidence: 99%

Detection of Diseased Plants by Analysis of Volatile Organic Compound Emission

Jansen

Wildt

Kappers

et al. 2011

Annu. Rev. Phytopathol.

102

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This review focuses on the detection of diseased plants by analysis of volatile organic compound (VOC) emissions. It includes an overview of studies that report on the impact of infectious and noninfectious diseases on these emissions and discusses the specificity of disease-induced emissions. The review also provides an overview of processes that affect the gas balance of plant volatiles, including their loss processes. These processes are considered as important because they contribute to the time-dynamic concentration profiles of plant-emitted volatiles. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on agricultural application. Dynamic sampling coupled with gas chromatography and followed by an appropriate detector is considered as the most appropriate method for application in agriculture. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in agricultural practice. However, to apply such an instrument in agriculture remains a challenge, mainly due to high costs.

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Section: Emissions As a Results Of Infectious Plant Diseasesmentioning

confidence: 99%

Detection of Diseased Plants by Analysis of Volatile Organic Compound Emission

Jansen

Wildt

Kappers

et al. 2011

Annu. Rev. Phytopathol.

102

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“…Similarly, new food-analysis e-nose methods are being developed to detect changes in VOCs released from foods and beverages in storage to assess shelf-life [346,397,398] and quality [185,206,399–403], and for chemical analyses [404,405], classifications [227,232,346,406,407], and discriminations [162,218,228,408] of food types, varieties and brands. Electronic-nose applications to detect plant pests in preharvest and postharvest crops and tree species continue to expand to include new insect [54–61] and disease [111,112,339,409–413] pests, primarily microbial plant pathogens, beyond those originally reported by Wilson et al [2,106,107]. In the macroenvironments adjacent to industrial plants and indoor working spaces within associated food- and fiber-production facilities, e-noses increasingly are being utilized to monitor air quality to detect hazardous chemicals [68–70,76,77,80,414–419], explosives and flammable gases [29,64], pollutants [420–422] and other VOCs that threaten human health.…”

Section: Discussionmentioning

confidence: 99%

Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry

Wilson

2013

Sensors

279

178

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Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

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“…During swelling, the distance between the conductive carbon black particles increases and hence, increasing the resistance of the composite [27,28]. This type of e-nose polymer sensor had been employed for many applications, including quality control in food industry, plant disease detection and biomedical sample discrimination [24,29,30]. …”

Section: Methodsmentioning

confidence: 99%

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

Subari

Mohamad–Saleh

Shakaff

et al. 2012

Sensors

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This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

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Intelligent electronic nose system for basal stem rot disease detection

Cited by 97 publications

References 8 publications

Detection of Diseased Plants by Analysis of Volatile Organic Compound Emission

Detection of Diseased Plants by Analysis of Volatile Organic Compound Emission

Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

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