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An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft c...

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Detalles Bibliográficos
Autores principales: Aladhadh, Suliman, Habib, Shabana, Islam, Muhammad, Aloraini, Mohammed, Aladhadh, Mohammed, Al-Rawashdeh, Hazim Saleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785034/
https://www.ncbi.nlm.nih.gov/pubmed/36560117
http://dx.doi.org/10.3390/s22249749
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author Aladhadh, Suliman
Habib, Shabana
Islam, Muhammad
Aloraini, Mohammed
Aladhadh, Mohammed
Al-Rawashdeh, Hazim Saleh
author_facet Aladhadh, Suliman
Habib, Shabana
Islam, Muhammad
Aloraini, Mohammed
Aladhadh, Mohammed
Al-Rawashdeh, Hazim Saleh
author_sort Aladhadh, Suliman
collection PubMed
description Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model’s effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production.
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spelling pubmed-97850342022-12-24 An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity Aladhadh, Suliman Habib, Shabana Islam, Muhammad Aloraini, Mohammed Aladhadh, Mohammed Al-Rawashdeh, Hazim Saleh Sensors (Basel) Article Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model’s effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production. MDPI 2022-12-12 /pmc/articles/PMC9785034/ /pubmed/36560117 http://dx.doi.org/10.3390/s22249749 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aladhadh, Suliman
Habib, Shabana
Islam, Muhammad
Aloraini, Mohammed
Aladhadh, Mohammed
Al-Rawashdeh, Hazim Saleh
An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title_full An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title_fullStr An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title_full_unstemmed An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title_short An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity
title_sort efficient pest detection framework with a medium-scale benchmark to increase the agricultural productivity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785034/
https://www.ncbi.nlm.nih.gov/pubmed/36560117
http://dx.doi.org/10.3390/s22249749
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