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...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9749 |
| _version_ | 1797455330818392064 |
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| author | Suliman Aladhadh Shabana Habib Muhammad Islam Mohammed Aloraini Mohammed Aladhadh Hazim Saleh Al-Rawashdeh |
| author_facet | Suliman Aladhadh Shabana Habib Muhammad Islam Mohammed Aloraini Mohammed Aladhadh Hazim Saleh Al-Rawashdeh |
| author_sort | Suliman Aladhadh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T15:52:52Z |
| format | Article |
| id | doaj.art-c1a825bcdac343bfb507897d560cb7dd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c1a825bcdac343bfb507897d560cb7dd2023-11-24T17:54:41ZengMDPI AGSensors1424-82202022-12-012224974910.3390/s22249749An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural ProductivitySuliman Aladhadh0Shabana Habib1Muhammad Islam2Mohammed Aloraini3Mohammed Aladhadh4Hazim Saleh Al-Rawashdeh5Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi ArabiaDepartment of Food Science and Human Nutrition, College of Agriculture and Veterinary Medicine, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Cyber Security, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi ArabiaInsect 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.https://www.mdpi.com/1424-8220/22/24/9749artificial intelligencecrop diseasesconvolutional neural networkFaster-RCNNmachine learningobject detection |
| spellingShingle | Suliman Aladhadh Shabana Habib Muhammad Islam Mohammed Aloraini Mohammed Aladhadh Hazim Saleh Al-Rawashdeh An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity Sensors artificial intelligence crop diseases convolutional neural network Faster-RCNN machine learning object detection |
| 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 | artificial intelligence crop diseases convolutional neural network Faster-RCNN machine learning object detection |
| url | https://www.mdpi.com/1424-8220/22/24/9749 |
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