Open Access

ARTICLE

Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

Mohamed Esmail Karar^1,2, Omar Reyad^1,3,*, Abdel-Haleem Abdel-Aty⁴, Saud Owyed⁵, Mohd F. Hassan⁶

1 College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia
2 Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt
3 Faculty of Science, Sohag University, Sohag, 82524, Egypt
4 Department of Physics, College of Sciences, University of Bisha, Bisha, 61922, Saudi Arabia
5 Department of Mathematics, College of Sciences, University of Bisha, Bisha, 61922, Saudi Arabia
6 Department of Computer and Information Science, CeRDaS, Universiti Teknologi Petronas, Malaysia

* Corresponding Author: Omar Reyad. Email:

(This article belongs to the Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)

Computers, Materials & Continua 2021, 69(3), 4095-4111. https://doi.org/10.32604/cmc.2021.019059

Received 31 March 2021; Accepted 09 May 2021; Issue published 24 August 2021

Abstract

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the infested cases. Then, the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model, i.e., InceptionResNet-V2. The proposed infestation classifier has been successfully validated on the public TreeVibes database. It includes total short recordings of 1754 samples, such that the clean and infested signals are 1754 and 731 samples, respectively. Compared to other deep learning models in the literature, our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio recordings. The resulted classification accuracy score was 97.18%. Using 10-fold cross validation, the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53% and ±1.69, respectively. Applying the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

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