Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors

Gardner, Julian W.; Hines, Evor L.; Molinier, F.; Bartlett, P. N.; Mottram, Toby

doi:10.1049/ip-smt:19990100

Cited by 24 publications

(13 citation statements)

References 8 publications

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“…Analysis of dairy patterns from a large biological database has been performed using neural networks [20] 14. Health predictions of dairy cattle from breath samples have been carried out using neural network models [21] 15. Neural network approach has been used to synthesize an online feedback optimal medication strategy for the parturient paresis problem of cows [22].…”

Section: Introductionmentioning

confidence: 99%

Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

Sharma

Kasana

2006

Neural Comput & Applic

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In this paper, two connectionist models are proposed based on different learning paradigms, viz., back propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) to predict the first lactation 305-day milk yield (FLMY305) in Karan Fries (KF) dairy cattle. Also, a conventional multiple linear regression (MLR) model is developed for the prediction. In this study, all the models have been developed using a scientifically determined optimum dataset of representative breeding traits of the cattle. The prediction performances of the connectionist models are compared with that of the conventional model. This study shows that the RBFNN model performs relatively better than the MLR model. However, the BPNN model performs more or less in the close vicinity of the conventional MLR model. Hence, it is inferred that the connectionist models have potential as an alternative to the conventional models for predicting FLMY305 in KF cattle.

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Section: Introductionmentioning

confidence: 99%

Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

Sharma

Kasana

2006

Neural Comput & Applic

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“…The results confirmed the applicability of this method. It is a common method that the interferences of temperature and humidity were suppressed by integrating corresponding sensors into the sensor array [ 23 , 24 , 25 , 26 ].…”

Section: Methods For Suppressing the Interference Caused By Changementioning

confidence: 99%

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Liang

Tian

Yang

et al. 2018

Sensors

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Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

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“…E-nose devices can be successfully used to demonstrate the presence of microbial pathogens [ 29 ], parasites [ 30 ], as well as changes in the metabolism of parasitized organisms [ 31 ]. It seemed, therefore, that the multi-sensor system could also work in diagnosing varroosis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Bąk

Wilk

Artiemjew

et al. 2020

Sensors

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Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class—empty chamber (13 objects), 2nd class—fragments of combs containing brood sick with varroosis (19 objects), and 3rd class—fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)—with default settings in RSES tool—was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments—in multiple Monte Carlo Cross Validation model—using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.

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Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors

Cited by 24 publications

References 8 publications

Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

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