Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition
Wheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced comp...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10613379/ |
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| author | Muhammad Islam Mohammed Aloraini Shabana Habib Meshari D. Alanazi Ishrat Khan Aqib Khan |
| author_facet | Muhammad Islam Mohammed Aloraini Shabana Habib Meshari D. Alanazi Ishrat Khan Aqib Khan |
| author_sort | Muhammad Islam |
| collection | DOAJ |
| description | Wheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced computer vision is crucial for safeguarding wheat quality. However, the literature relied on inadequate feature selection acquired from computationally expensive backbones followed by shallow layered networks. It is resultantly limiting their capacity to recognize and prioritize diseased areas effectively. Therefore, this paper introduces an optimal features-assisted lightweight framework that integrates EfficientNet-B3 with a spatial attention (SA) mechanism to capture healthy and unhealthy patterns effectively. The proposed framework harnesses this capability to address the vital regions affected by wheat diseases via an optimized, lightweight, and attentive network. Subsequently, we thoroughly analyzed several backbone features to identify robust hyperparameters conducive to achieving our lightweight objective. Furthermore, we employed SA blocks to fortify the network, directing attention efficiently towards diseased regions. The efficacy of the proposed network is validated through comprehensive evaluations conducted on both our proposed and LWDCD2020 benchmarks. Comparative analyses with existing methods consistently showcase superiority, firmly establishing our proposed approach as a viable network for wheat disease recognition. additionally, we also collected our own dataset, namely the Wheat Disease Five Classes Classification Dataset (WD5CC), and included diverse images. |
| format | Article |
| id | doaj-art-fb173f7995de4156832c751c8eaa212a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fb173f7995de4156832c751c8eaa212a2025-08-20T03:07:00ZengIEEEIEEE Access2169-35362024-01-011215073915075310.1109/ACCESS.2024.343457510613379Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases RecognitionMuhammad Islam0https://orcid.org/0000-0002-2379-4451Mohammed Aloraini1https://orcid.org/0000-0002-1655-8098Shabana Habib2https://orcid.org/0000-0002-6543-2520Meshari D. Alanazi3Ishrat Khan4https://orcid.org/0000-0003-3036-991XAqib Khan5https://orcid.org/0009-0005-3171-9418Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaDepartment of Botany, Islamia College University, Peshawar, PakistanWheat serves as a crucial agricultural commodity and a primary dietary staple for numerous global populations. However, it faces persistent threats from various diseases targeting wheat leaves, ultimately impacting its production. Accurate and prompt automated disease diagnosis through advanced computer vision is crucial for safeguarding wheat quality. However, the literature relied on inadequate feature selection acquired from computationally expensive backbones followed by shallow layered networks. It is resultantly limiting their capacity to recognize and prioritize diseased areas effectively. Therefore, this paper introduces an optimal features-assisted lightweight framework that integrates EfficientNet-B3 with a spatial attention (SA) mechanism to capture healthy and unhealthy patterns effectively. The proposed framework harnesses this capability to address the vital regions affected by wheat diseases via an optimized, lightweight, and attentive network. Subsequently, we thoroughly analyzed several backbone features to identify robust hyperparameters conducive to achieving our lightweight objective. Furthermore, we employed SA blocks to fortify the network, directing attention efficiently towards diseased regions. The efficacy of the proposed network is validated through comprehensive evaluations conducted on both our proposed and LWDCD2020 benchmarks. Comparative analyses with existing methods consistently showcase superiority, firmly establishing our proposed approach as a viable network for wheat disease recognition. additionally, we also collected our own dataset, namely the Wheat Disease Five Classes Classification Dataset (WD5CC), and included diverse images.https://ieeexplore.ieee.org/document/10613379/Image processingintelligent systemsmart agriculturelightweight networkInternet of Thingsdeep learning |
| spellingShingle | Muhammad Islam Mohammed Aloraini Shabana Habib Meshari D. Alanazi Ishrat Khan Aqib Khan Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition IEEE Access Image processing intelligent system smart agriculture lightweight network Internet of Things deep learning |
| title | Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition |
| title_full | Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition |
| title_fullStr | Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition |
| title_full_unstemmed | Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition |
| title_short | Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases Recognition |
| title_sort | optimal features driven attention network with medium scale benchmark for wheat diseases recognition |
| topic | Image processing intelligent system smart agriculture lightweight network Internet of Things deep learning |
| url | https://ieeexplore.ieee.org/document/10613379/ |
| work_keys_str_mv | AT muhammadislam optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition AT mohammedaloraini optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition AT shabanahabib optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition AT mesharidalanazi optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition AT ishratkhan optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition AT aqibkhan optimalfeaturesdrivenattentionnetworkwithmediumscalebenchmarkforwheatdiseasesrecognition |