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Article

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

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Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia
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Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32102 Čačak, Serbia
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Institute Mihailo Pupin d.o.o., Volgina 15, 11060 Belgrade, Serbia
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IHP-Leibniz-Institut für Innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt Oder, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Asim Biswas and Viacheslav Adamchuk
Sensors 2021, 21(14), 4846; https://doi.org/10.3390/s21144846
Received: 30 April 2021 / Revised: 30 June 2021 / Accepted: 13 July 2021 / Published: 16 July 2021
(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)
The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields. View Full-Text
Keywords: machine learning; pest insect appearance; temperature and relative humidity sensors; precision agriculture machine learning; pest insect appearance; temperature and relative humidity sensors; precision agriculture
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MDPI and ACS Style

Marković, D.; Vujičić, D.; Tanasković, S.; Đorđević, B.; Ranđić, S.; Stamenković, Z. Prediction of Pest Insect Appearance Using Sensors and Machine Learning. Sensors 2021, 21, 4846. https://doi.org/10.3390/s21144846

AMA Style

Marković D, Vujičić D, Tanasković S, Đorđević B, Ranđić S, Stamenković Z. Prediction of Pest Insect Appearance Using Sensors and Machine Learning. Sensors. 2021; 21(14):4846. https://doi.org/10.3390/s21144846

Chicago/Turabian Style

Marković, Dušan, Dejan Vujičić, Snežana Tanasković, Borislav Đorđević, Siniša Ranđić, and Zoran Stamenković. 2021. "Prediction of Pest Insect Appearance Using Sensors and Machine Learning" Sensors 21, no. 14: 4846. https://doi.org/10.3390/s21144846

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