Classifying Infected Palms with Dubas's Bug Based on Artificial Intelligence

Al-Mahmood, Abdullah Mazin; Shahadi, Haider Ismael; Khayeat, Ali Retha Hasoon

doi:10.1109/icecet55527.2022.9872955

Cited by 3 publications

(3 citation statements)

References 9 publications

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“… Traditional methods of diagnosis the dubas pest requires expert knowledge and spend a long time to diagnose vast cultivated areas, so this dataset can be used in automatic diagnosis of the pest Datasets can be used in machine learning, and deep learning to build a powerful insect taxonomy [4] . Standalone systems for dubas pest detection and treatment can employ datasets in the training phase then use a drone for real-time diagnosis and treatments [5] . This leads to better accuracy, less effort, and reduce pesticides that positively reflect the environmental impact and cost savings [6] .…”

Section: Value Of the Datamentioning

confidence: 99%

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Image dataset of infected date palm leaves by dubas insects

AL-Mahmood,

Shahadi,

Hasoon Khayeat

2023

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

“…Standalone systems for dubas pest detection and treatment can employ datasets in the training phase then use a drone for real-time diagnosis and treatments [5] . This leads to better accuracy, less effort, and reduce pesticides that positively reflect the environmental impact and cost savings [6] .…”

Section: Value Of the Datamentioning

confidence: 99%

Image dataset of infected date palm leaves by dubas insects

AL-Mahmood,

Shahadi,

Hasoon Khayeat

2023

Data in Brief

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“…Generally, farmers did not have some automatic method that offers evident indicators around infected palm trees to lead to 25-40% loss of yielding because of uncontrolled processes. Control of the RPW introduces an important challenge for entomologists because of the starting of current farming, specifically in the earlier identification of infected palms [3]. Visual analysis of palms is one of the major adaptable methods to detect infected palms.…”

Section: Introductionmentioning

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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