Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

Karar, Mohamed Esmail; Reyad, Omar; Abdel‐Aty, Abdel‐Haleem; Owyed, Saud; Hassan, Mohd Fadzil Abu

doi:10.32604/cmc.2021.019059

Cited by 12 publications

(9 citation statements)

References 34 publications

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“…The final stage comprises the use of diverse DL methods for classifying resultant images. Karar et al [15] introduced a novel IoTbased model for earlier sound identification of RPWs applying modified TL methods such as InceptionResNet_V2. Palm trees could be labelled depending on the count of sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

Arasi,

Almuqren,

Issaoui

et al. 2024

IEEE Access

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In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.

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Section: Related Workmentioning

confidence: 99%

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

Arasi,

Almuqren,

Issaoui

et al. 2024

IEEE Access

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“…However, there is no doubt that acoustic and vibrational sensors comprise the main scientific efforts to detect early RPW infestation. [17][18][19][20][21][22][23][24][25][26] This is also true for other notorious invasive woodboring beetles 27,28 or boring insects in wooden constructions. 29,30 The challenge of monitoring woodborers is derived in part from the illusiveness of the adult during the short period of egg laying.…”

Section: Introductionmentioning

confidence: 99%

“…Jaques 16 presented guidelines on visual inspection for the early detection of RPW infestation in Canary Island date palms. However, there is no doubt that acoustic and vibrational sensors comprise the main scientific efforts to detect early RPW infestation 17–26 . This is also true for other notorious invasive woodboring beetles 27,28 or boring insects in wooden constructions 29,30 …”

Section: Introductionmentioning

confidence: 99%

Seismic sensor‐based management of the red palm weevil Rhynchophorus ferrugineus in date palm plantations

Mendel,

Voet,

Modan

et al. 2023

Pest Management Science

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BACKGROUNDThe red palm weevil (RPW), Rhynchophorus ferrugineus, stands as one of the gravest threats to palm trees. The challenge in monitoring RPW primarily arises from the inconspicuous presence of larvae within the stem, often devoid of noticeable symptoms. This study delves into the integration of seismic sensors into RPW management in commercial date palm plantations. It explores whether the data garnered from the sensor domain, and its translation into the health status of date palms, can reliably inform precise decision‐making.RESULTSSensor values and damage indices, as gauged by the Agrint IoTree seismic sensor, vividly mirrored RPW colonization activity. They also accurately portrayed the impact of three distinct insecticides: imidacloprid, phosphine, and entomopathogenic nematodes. The seismic values and damage index of the healthy, untreated palms strongly supported the decision to pursue tree recovery. Furthermore, this facilitated the computation of recovery pace discrepancies across the tested treatments, measured in the number of days required for tree restoration.CONCLUSIONSOur findings underscore the practicality of employing seismic sensors, exemplified by the IoTree system and its network services, to both monitor and assess palm health conditions. Furthermore, it validates their efficacy in evaluating the efficiency of management strategies adopted against the weevil, all grounded in the wealth of sensor‐derived data.This article is protected by copyright. All rights reserved.

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“…Sun et al [22] proposed a lightweight convolutional neural network (CNN) to automatically identify the boring vibrations of Semanotus bifasciatus and Eucryptorrhynchus brandti larvae. Karar et al [23] proposed an IoT-based framework for the early detection of red palm weevils using a fine-tuned classifier, InceptionResNet-V2 [24], which was trained through vibration data recorded by a Tree Vibes [13] recording device. Zhang et al [25] designed a neural network named TrunkNet for the purpose of identifying the existence of Agrilus planipennis larvae in trunks by their vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring

Shi

Chen

Zhang

et al. 2023

Forests

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Wood-boring beetles are among the most destructive forest pests. The larvae of some species live in the trunks and are covered by bark, rendering them difficult to detect. Early detection of these larvae is critical to their effective management. A promising surveillance method is inspecting the vibrations induced by larval activity in the trunk to identify whether it is infected. As convenient as it seems, it has a significant drawback. The identification process is easily disrupted by environmental noise and results in low accuracy. Previous studies have proven the feasibility and necessity of adding an enhancement procedure before identification. To this end, we proposed a small yet powerful boring vibration enhancement network based on deep learning. Our approach combines frequency-domain and time-domain enhancement in a stacked network. The dataset employed in our study comprises the boring vibrations of Agrilus planipennis larvae and various environmental noises. After enhancement, the SNR (signal-to-noise ratio) increment of a boring vibration segment reaches 18.73 dB, and our model takes only 0.46 s to enhance a 5 s segment on a laptop CPU. The accuracy of several well-known classification models showed a substantial increase using clips enhanced by our model. All experimental results proved our contribution to the early detection of larvae.

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Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

Cited by 12 publications

References 34 publications

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

Seismic sensor‐based management of the red palm weevil Rhynchophorus ferrugineus in date palm plantations

Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring

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