Gas Sensor Array and Classifiers as a Means of Varroosis Detection

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.

[1]  S. B. Savvin,et al.  Chemical sensors: definitions and classification , 1991 .

[2]  Heping Zhu,et al.  Plant Pest Detection Using an Artificial Nose System: A Review , 2018, Sensors.

[3]  Andrzej Szczurek,et al.  Portable Sensing of Organic Vapours based on a Single Semiconductor Sensor , 2014, SENSORNETS.

[4]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[5]  Mahdi Ghasemi-Varnamkhasti,et al.  Selection of an optimized metal oxide semiconductor sensor (MOS) array for freshness characterization of strawberry in polymer packages using response surface method (RSM) , 2019, Postharvest Biology and Technology.

[6]  A. Hierlemann,et al.  Higher-order Chemical Sensing , 2007 .

[7]  Teerakiat Kerdcharoen,et al.  Development and application of electronic nose for agricultural robot , 2013, 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[8]  Kim Bjerge,et al.  A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony , 2019, Comput. Electron. Agric..

[9]  K. Persaud,et al.  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose , 1982, Nature.

[10]  Ahmad Khalilian,et al.  Development of a Portable Electronic Nose for Detection of Cotton Damaged by Nezara viridula (Hemiptera: Pentatomidae) , 2014 .

[11]  D. Jayas,et al.  Feasibility of the application of electronic nose technology to detect insect infestation in wheat , 2013 .

[12]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Dean Zhao,et al.  Research on Recognition System of Agriculture Products Gas Sensor Array and its Application , 2012 .

[14]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[15]  Mikkel Kragh Hansen,et al.  Automatic behaviour analysis system for honeybees using computer vision , 2016, Comput. Electron. Agric..

[16]  M. Słowińska,et al.  2D-DIGE proteomic analysis reveals changes in haemolymph proteome of 1-day-old honey bee (Apis mellifera) workers in response to infection with Varroa destructor mites , 2019, Apidologie.

[17]  G. Gilioli,et al.  Beekeeping and honey bee colony health: A review and conceptualization of beekeeping management practices implemented in Europe , 2019, Science of The Total Environment.

[18]  I. Fries,et al.  Comparison of diagnostic methods for detection of low infestation levels ofVarroa jacobsoni in honey-bee (Apis mellifera) colonies , 1991, Experimental & Applied Acarology.

[19]  Simone Orcioni,et al.  Multi-sensor platform for real time measurements of honey bee hive parameters , 2019, IOP Conference Series: Earth and Environmental Science.

[20]  P. Neumann,et al.  The COLOSS BEEBOOK Volume II, Standard methods for Apis mellifera pest and pathogen research: Introduction , 2013 .

[21]  Honey bee diseases and pests : a practical guide 4 , 2007 .

[22]  Aleksejs Zacepins,et al.  Challenges in the development of Precision Beekeeping , 2015 .

[23]  G. Borsuk,et al.  Changes in the bioelement content of summer and winter western honeybees (Apis mellifera) induced by Nosema ceranae infection , 2018, PloS one.

[24]  Monika Maciejewska,et al.  Semiconductor gas sensor as a detector of Varroa destructor infestation of honey bee colonies - Statistical evaluation , 2019, Comput. Electron. Agric..

[25]  Robert Rusinek,et al.  Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal. , 2019, Journal of food science.

[26]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[27]  Kang Tu,et al.  Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry , 2014 .

[28]  N. Carreck,et al.  Honey bee colony losses , 2010 .

[29]  J. Wang,et al.  Detection of insect infestations in paddy field using an electronic nose , 2011 .

[30]  W. C. Hoffmann,et al.  Identification of Stink Bugs Using an Electronic Nose , 2008 .

[31]  P. Hendrikx,et al.  Risk indicators affecting honeybee colony survival in Europe: one year of surveillance , 2016, Apidologie.