Development and application of electronic nose for agricultural robot

Siyang, Satetha; Lorwongtragool, Panida; Noosidum, Atirach; Wongchoosuk, Chatchawal; Kerdcharoen, Teerakiat

doi:10.1109/ecticon.2013.6559500

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…The particular focus is on bacterial, fungal, and viral infections, and insect damage. The E-nose has successfully detected the different soil volatile fingerprints in each depth level and insect pests [20]. Lan et al reported the identification of insect by using the E-nose [21].…”

Section: Introductionmentioning

confidence: 99%

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

Szczurek

Maciejewska

Bąk

et al. 2019

Sensors

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The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

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Section: Introductionmentioning

confidence: 99%

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

Szczurek

Maciejewska

Bąk

et al. 2019

Sensors

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“…The second part involves mounting an array of six gas sensors on a semi-autonomous six-wheeled robotic chassis to measure the true condition of the soil surface located at four different locations. Satetha et al [201] develop an e-nose for an agricultural smart robot. The e-nose is used to detect soil volatile fingerprints and pests at different depths.…”

Section: Mobile E-nose Systemsmentioning

confidence: 99%

Development of compact electronic noses: a review

Liu

Meng

Lilienthal

et al. 2021

Meas. Sci. Technol.

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An electronic nose (e-nose) is a measuring instrument that mimics human olfaction and outputs ‘fingerprint’ information of mixed gases or odors. Generally speaking, an e-nose is mainly composed of two parts: a gas sensing system (gas sensor arrays, gas transmission paths) and an information processing system (microprocessor and related hardware, pattern recognition algorithms). It has been more than 30 years since the e-nose concept was introduced in the 1980s. Since then, e-noses have evolved from being large in size, expensive, and power-hungry instruments to portable, low cost devices with low power consumption. This paper reviews the development of compact e-nose design and calculation over the last few decades, and discusses possible future trends. Regarding the compact e-nose design, which is related to its size and weight, this paper mainly summarizes the development of sensor array design, hardware circuit design, gas path (i.e. the path through which the mixed gases to be measured flow inside the e-nose system) and sampling design, as well as portable design. For the compact e-nose calculation, which is directly related to its rapidity of detection, this review focuses on the development of on-chip calculation and wireless computing. The future trends of compact e-noses include the integration with the internet of things, wearable e-noses, and mobile e-nose systems.

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“…The previous three decades have evidenced the proliferation of designing electronic noses (e-Noses) to mimic the mammalian olfactory system (MOS) [1]- [3]. A variety of real-life paradigms saw the spike in applications of e-Nose for areas such as environment [4], healthcare [5], automobile [6], agriculture [7], foods & beverages [8], [9], beauty & cosmetics [10], robotics [11], safety & security [12], forensics [13], textiles [14], coal mines [15], etc. A brief introduction to e-Nose could be beneficial to the readers to understand the hypothesis behind virtual gas sensors (VGSs) and virtual gas sensor responses (VGSRs).…”

Section: Introductionmentioning

confidence: 99%

A novel data-driven technique to produce multi- sensor virtual responses for gas sensor array-based electronic noses

Srivastava

Chaudhri

Rajput

et al. 2023

Journal of Electrical Engineering

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Accurate detection of gas/odor requires highly selective gas sensor. However, the high-performance classification of gases/odors can be achieved using partial-selective gas sensors. Since 1980s, an array of broadly tuned (partial-selective) gas sensors have been used in several fields of science and engineering, and the resulting gas sensing systems (GSS) are popularly known as electronic noses (e-Noses). The combination of similar or different sensors in the array indirectly compensates for the requirement of high selectivity in GSS. Further, e-Nose’s performance inevitably depends on the salient features drawn from the initial responses of the gas sensor array (GSA). So obtained features are referred to as the responses of virtual sensors (VS). In this paper, we have proposed the three-input and three-output (TITO) technique to derive efficient virtual sensor responses (VSRs) which outperform its well-published peer technique. A GSA consisting of four elements is used to demonstrate the proposed technique. Our proposed technique augments the VSRs by four times compared to its peer. The efficacy of our proposed technique has been tested using nine fundamental classifiers, viz., linear support vector machine (100%), decision tree (97.5%), multi-layer perceptron neural network (100%), K-nearest neighbor (85%), logistic regression (100%), Gaussian process with radial basis function (95%), linear discriminant analysis (97.5%), random forest (100%), and AdaBoost (95%). Ten-fold cross-validation has been used to minimize the biasing impact of the intra- and inter-class variance. With the result, four classifiers successfully provide an accuracy of 100 percent. Hence, we have proposed and vindicated an efficient technique.

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Development and application of electronic nose for agricultural robot

Cited by 10 publications

References 18 publications

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

Development of compact electronic noses: a review

A novel data-driven technique to produce multi- sensor virtual responses for gas sensor array-based electronic noses

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