@article{scholars15679,
           title = {Intelligent IoT-Aided early sound detection of red palmWeevils},
             doi = {10.32604/cmc.2021.019059},
          number = {3},
            note = {cited By 5},
          volume = {69},
           pages = {4095--4111},
       publisher = {Tech Science Press},
         journal = {Computers, Materials and Continua},
            year = {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 carefullymounted 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{\^A}{$\pm$}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. {\^A}{\copyright} 2021 Tech Science Press. All rights reserved.},
        keywords = {Agricultural robots; Classification (of information); Deep learning; Forestry; Learning systems; Palmprint recognition; Sensor nodes; Transfer learning, 10-fold cross-validation; Agricultural process; Classification accuracy; Internet of Things (IOT); Learning classifiers; On-line analysis; Standard deviation; Wireless communication technology, Internet of things},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113467989&doi=10.32604\%2fcmc.2021.019059&partnerID=40&md5=a90da84c49074b4c1657d669f871ef78},
            issn = {15462218},
          author = {Karar, M. E. and Reyad, O. and Abdel-Aty, A.-H. and Owyed, S. and Hassan, M. F.}
}