title: Enhanced monitoring of insect pests in stored rice based on their sound signature and evaluation of attractants for storage insects
Creators: name: Balingbing, Carlito Baldo
Publisher: name: KOBRA Universität Kassel
Year: 2025
Subjects: subject: acoustic system
subject: MEMS microphone
subject: machine learning
subject: sound signature
subject: rice storage
subject: insects
subject: attractants
subject: postharvest losses
subject: LED
classificationCode: 000
subject: 000
classificationCode: 570
subject: 570
classificationCode: MEMS
subject: MEMS
classificationCode: Insekten
subject: Insekten
classificationCode: Convolutional Neural Network
subject: Convolutional Neural Network
classificationCode: Vorratsschädling
subject: Vorratsschädling
classificationCode: Getreidelagerung
subject: Getreidelagerung
classificationCode: Maschinelles Lernen
subject: Maschinelles Lernen
classificationCode: Ernteverlust
subject: Ernteverlust
classificationCode: Insektenlockstoff
subject: Insektenlockstoff

description: <p>This study successfully evaluated the use of a MEMS microphone as part of an acoustic device to record the sounds produced by insect pests in stored rice grains. The device effectively captured the subtle sounds of small insects infesting the grains, particularly when recordings were conducted inside a soundproof chamber that minimized background noise. Using machine learning, specifically a multi-layer convolutional neural network (CNN), insect species were classified based on these baseline recordings. The CNN model achieved an 84.51% classification accuracy by extracting spectrogram features from the recorded insect sounds. A manual hyperparameter tuning technique was applied to achieve the optimum specifications of the CNN model which resulted to highest classification accuracy. Furthermore, the pre-trained CNN model was deployed into a web application to classify and predict audio recordings, with results displayed graphically with the main objective of providing real-time information to farmers and end-users for quick implementation of corrective actions to insect infestations in stored grains.
The acoustic device was also tested in actual grain storage systems infested with storage insects, where the use of attractants significantly enhanced insect aggregation near the device. In actual storage environments, insects are typically dispersed making sound detection a real challenge specially for a single or few insects, as their movement sounds may be too faint to register and the sound of a single insect crawling is often too weak to detect. This underscores the importance of using attractants to lure insects to a single location, making sound detection more feasible.
Of the four physical attractants tested, the red LED was the most effective in luring Rhyzopertha dominica and Sitophilus oryzae, while an air blower mechanism using a low-power computer fan was successful in attracting Tribolium castaneum into a pitfall trap. In separate replicated trials, comparing the red and blue LEDs, the blue LED captured more R. dominica and T. castaneum than the red LED. These two LED colors can be used in combination with the acoustic device to target specific insect species. These simple, eco-friendly attractants, constructed from locally available materials, offer an accessible solution for farmers and end-users dealing with insect infestations in stored grains.
When used in combination with the acoustic device, these attractants enhance the system’s performance by drawing hidden insects towards traps, enabling their acoustic detection. This integrated approach allows for continuous monitoring and helps reduce insect populations over time by capturing and eliminating pests. Its potential is even greater in larger storage systems, such as commercial warehouses and rice mills, is significant, though further field testing in larger spaces is required to confirm its efficacy.
In this study, the acoustic device and attractants were tested in a 1-ton storage system with a height and diameter of 1 meter. Both the red LED and air blower effectively attracted target insects within this space, and the acoustic device successfully recorded the sounds of trapped insects, demonstrating its potential for future automation. The target insects in this study which includes (R. dominica, S. oryzae and T. castaneum) were characterized according to the sound characteristics similar to how human produced sounds are characterized. The frequency ranges of the sound produced by the three insect species were calculated as – (a) R. dominica: 120-355 Hz; (b) S. oryzae: 117-441 Hz; and (c) T. castaneum: 115-434 Hz.  The input data to the CNN model was extracted by calculating the Short-Time Fourier Transform (STFT) of the audio recordings that generate the time-frequency image representation of the sound recordings which also include information on the sound’s intensity level. By integrating machine learning via the deep learning CNN algorithm, real-time insect detection could be realized through a mobile or web application, allowing warehouse managers to receive timely notifications of insect infestations and implement targeted pest control measures.
Additionally, a prototype ePADDY web application was developed for the detection and classification of insect pest sound recordings in rice storage. This prototype application will also be integrated with an edge device as an embedded system for real-time insect pest detection and classification using a MEMS microphone. Further improvements will focus on refining pre-processing techniques to enhance model performance and classification accuracy. This advancement aims to provide a cost-effective, portable solution for pest management in postharvest storage facilities, facilitating rapid responses to pest infestations and supporting the protection of stored grains. However, this area requires further refinement and rigorous field testing to ensure its reliability and robustness.
While this study focused on three major insect pests of stored grains, many other species also pose a threat. The effectiveness of the attractants and acoustic device in luring and detecting additional pests should be further evaluated. Trap designs may need modifications to capture flying insects, such as moths, or larvae. Furthermore, the device’s capacity to capture sounds from these other insect species should be explored. Future research should aim to refine the insect classification algorithm and enhance the device’s overall performance. Transfer learning techniques in deep learning models, such as CNN, could further improve accuracy and align predictions more closely with real-world conditions, as highlighted by previous studies (Buchanan et al., 2021; Mishra and Passos, 2021; Rochadiani et al., 2024).</p>
description: <p>In dieser Studie wurde der Einsatz eines MEMS-Mikrofons in einem akustischen Gerät zur Erkennung von Insektenschädlingen in gelagertem Reis untersucht. Das Gerät konnte selbst schwache Geräusche der Insekten zuverlässig erfassen, insbesondere in einer schalldichten Kammer zur Reduzierung von Umgebungsgeräuschen. Die akustischen Daten wurden in Spektrogramme umgewandelt und mit einem Convolutional Neural Network (CNN) analysiert, das eine Klassifizierungsgenauigkeit von 84,51 % erreichte. Dieses Modell wurde in eine Webanwendung integriert, die eine visuelle Darstellung zur Insektenidentifikation in Echtzeit bietet.
Feldversuche zeigten, dass physische Lockstoffe die Wirksamkeit des Systems deutlich verbesserten. Rote LEDs zogen Rhyzopertha dominica und Sitophilus oryzae an, während ein leistungsschwacher Ventilator besonders bei Tribolium castaneum wirkte. In weiteren Tests zeigte sich, dass blaues Licht effektiver war für R. dominica und T. castaneum. Die Kombination aus akustischer Erkennung und gezielter Anlockung erlaubt eine artspezifische Erfassung. Diese Lockstoffe wurden aus lokal verfügbaren, umweltfreundlichen Materialien gefertigt und sind für Landwirte leicht zugänglich. Die Integration der Lockstoffe führte zu einer verbesserten Insektenerkennung, da mehr Tiere zum akustischen System gelockt wurden. Dieses System ermöglicht nicht nur eine kontinuierliche Überwachung, sondern trägt auch zur Reduktion der Schädlingspopulation bei. Das System wurde in einem 1-Tonnen-Lagerbehälter getestet, wobei sowohl LEDs als auch Ventilatoren zur erfolgreichen Anlockung
beitrugen. Die Geräuscherfassung kann künftig automatisiert werden, wobei mobile und webbasierte Anwendungen eine rechtzeitige Warnung für Lagermanager ermöglichen. Die entwickelte Webanwendung namens ePADDY dient der Klassifizierung und Insektenmeldung in Reislagerstätten. Um ihre Zuverlässigkeit sicherzustellen, sind jedoch weitere Entwicklungen und Feldvalidierungen erforderlich. Obwohl sich die Studie auf drei Hauptschädlinge konzentrierte, sollte die Eignung des Systems für andere Insektenarten wie fliegende Insekten oder Larven in zukünftigen Studien bewertet werden. Zudem könnte der Einsatz von Transfer Learning in Deep-Learning-Modellen die Genauigkeit und Anwendbarkeit in realen Lagerbedingungen weiter verbessern (Buchanan et al., 2021; Mishra und Passos, 2021; Rochadiani et al., 2024)</p>