Citation

Ali, Md. Akkas and Sayeed, Md Shohel and Abdul Razak, Siti Fatimah (2025) Employing IoT and pest sound analysis with multi-feature and multi-deep learning networks for detecting, preventing and controlling the pests in expansive farmland. International Journal of Machine Learning and Cybernetics. ISSN 1868-8071

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Abstract

The agriculture sectors, which account for approximately 50% of the worldwide economic production, are the fundamental cornerstone of each nation. The significance of precision agriculture cannot be understated in assessing crop conditions and identifying suitable treatments in response to diverse pest infestations. The conventional method of pest identification exhibits instability and yields subpar levels of forecast accuracy. Nevertheless, the monitoring techniques frequently exhibit invasiveness, require significant time and resources, and are susceptible to various biases. Numerous insect species can emit distinct sounds, which can be readily identified and recorded with minimal expense or exertion. Applying deep learning techniques enables the automated detection and classification of insect sounds derived from field recordings, hence facilitating the monitoring of biodiversity and the assessment of species distribution ranges. The current research introduces an innovative method for identifying and detecting pests through IoT-based computerized modules that employ an integrated deep-learning methodology using the dataset comprising audio recordings of insect sounds. This included techniques, the DTCDWT, Butterworth filter method, the Blackman–Nuttall window, the Savitzky–Golay filter, FFT, DFT, STFT, MFCC, BFCC, LFCC, acoustic detectors, and PID sensors. The proposed research integrated the MF-MDLNet to train, test, and validate data. 9600 pest auditory sounds were examined to identify their unique characteristics and numerical properties. The recommended system designed and implemented the ultrasound generator, with a programmable frequency and control panel for preventing and controlling pests, and a solar-charging system for supplying power to connected devices in the networks spanning large farming areas. The suggested approach attains an accuracy (99.82%), a sensitivity (99.94%), a specificity (99.86%), a recall (99.94%), an F1 score (99.89%), and a precision (99.96%). The findings of this study demonstrate a significant enhancement compared to previous scholarly investigations, including VGG 16, VOLOv5s, TSCNNA, YOLOv3, TrunkNet, DenseNet, and DCNN.

Item Type:	Article
Uncontrolled Keywords:	BFCC · MF-MDLNet · IoT · Sound analysis · Pest detection
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Jun 2025 02:37
Last Modified:	30 Jun 2025 02:37
URII:	http://shdl.mmu.edu.my/id/eprint/14145

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