Datum
2024-08-09Autor
Balingbing, Carlito B.Kirchner, SaschaSiebald, HubertusKaufmann, Hans-HermannGummert, MartinVan Hung, NguyenHensel, OliverSchlagwort
600 Technik 630 Landwirtschaft, Veterinärmedizin Auditorisches SystemMikrofonMEMSSchädlingReisLagerungMaschinelles LernenConvolutional Neural NetworkMetadata
Zur Langanzeige
Aufsatz
Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device
Zusammenfassung
Studies reported that 12–40% of stored grains are lost due to insects, but the use of early detection devices such as acoustic sensors can guide subsequent storage management to reducing losses. Acoustic detection can directly identify the cause of damage (i.e., insects) in stored grains rather than the effect (e.g., RH, temperature) and it is capable of handling high information density due to the broad frequency band and the different sound levels. This research addresses the question if the use of micro-electromechanical system (MEMS) microphone can detect insect sound in stored grains, predict insects’ presence and classify insects according to species with the application of a multi-layer convolutional neural network (CNN) algorithm.
We adapted the acoustic sensor from the Smart Apiculture Management Services (SAMS) project using the Adafruit SPH0645, an inexpensive MEMS microphone that was used to detect insect pests in stored rice grain. The recorded sounds of major insect pests (adult stage) in stored paddy grains, namely, lesser grain borer (Rhyzopertha dominica, Fabricius), rice weevil (Sitophilus oryzae, Linnaeus), and red flour beetle (Tribolium castaneum, Herbst) were characterized using spectrogram profiles. Machine learning technique was applied using CNN with an average accuracy of 84.51% to classify insect pests from the emitted sound profiles.
The use of an acoustic detection system and the application of a CNN classification model provides an efficient method of detecting hidden insects in stored grains that can guide farmers and end-users in implementing appropriate and timely insect pest control without applying harmful chemicals in stored grains.
We adapted the acoustic sensor from the Smart Apiculture Management Services (SAMS) project using the Adafruit SPH0645, an inexpensive MEMS microphone that was used to detect insect pests in stored rice grain. The recorded sounds of major insect pests (adult stage) in stored paddy grains, namely, lesser grain borer (Rhyzopertha dominica, Fabricius), rice weevil (Sitophilus oryzae, Linnaeus), and red flour beetle (Tribolium castaneum, Herbst) were characterized using spectrogram profiles. Machine learning technique was applied using CNN with an average accuracy of 84.51% to classify insect pests from the emitted sound profiles.
The use of an acoustic detection system and the application of a CNN classification model provides an efficient method of detecting hidden insects in stored grains that can guide farmers and end-users in implementing appropriate and timely insect pest control without applying harmful chemicals in stored grains.
Zitierform
In: Computers and Electronics in Agriculture Volume 225 (2024-08-09) eissn:1872-7107Förderhinweis
Gefördert im Rahmen des Projekts DEALZitieren
@article{doi:10.17170/kobra-2024082310708,
author={Balingbing, Carlito B. and Kirchner, Sascha and Siebald, Hubertus and Kaufmann, Hans-Hermann and Gummert, Martin and Van Hung, Nguyen and Hensel, Oliver},
title={Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device},
journal={Computers and Electronics in Agriculture},
year={2024}
}
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2024-09-02T13:11:15Z 2024-09-02T13:11:15Z 2024-08-09 doi:10.17170/kobra-2024082310708 http://hdl.handle.net/123456789/16014 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ acoustic system MEMS microphone pests in rice storage machine learning CNN 600 630 Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device Aufsatz Studies reported that 12–40% of stored grains are lost due to insects, but the use of early detection devices such as acoustic sensors can guide subsequent storage management to reducing losses. Acoustic detection can directly identify the cause of damage (i.e., insects) in stored grains rather than the effect (e.g., RH, temperature) and it is capable of handling high information density due to the broad frequency band and the different sound levels. This research addresses the question if the use of micro-electromechanical system (MEMS) microphone can detect insect sound in stored grains, predict insects’ presence and classify insects according to species with the application of a multi-layer convolutional neural network (CNN) algorithm. We adapted the acoustic sensor from the Smart Apiculture Management Services (SAMS) project using the Adafruit SPH0645, an inexpensive MEMS microphone that was used to detect insect pests in stored rice grain. The recorded sounds of major insect pests (adult stage) in stored paddy grains, namely, lesser grain borer (Rhyzopertha dominica, Fabricius), rice weevil (Sitophilus oryzae, Linnaeus), and red flour beetle (Tribolium castaneum, Herbst) were characterized using spectrogram profiles. Machine learning technique was applied using CNN with an average accuracy of 84.51% to classify insect pests from the emitted sound profiles. The use of an acoustic detection system and the application of a CNN classification model provides an efficient method of detecting hidden insects in stored grains that can guide farmers and end-users in implementing appropriate and timely insect pest control without applying harmful chemicals in stored grains. open access Balingbing, Carlito B. Kirchner, Sascha Siebald, Hubertus Kaufmann, Hans-Hermann Gummert, Martin Van Hung, Nguyen Hensel, Oliver doi:10.1016/j.compag.2024.109297 Auditorisches System Mikrofon MEMS Schädling Reis Lagerung Maschinelles Lernen Convolutional Neural Network publishedVersion eissn:1872-7107 Computers and Electronics in Agriculture Volume 225 false 109297
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