Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples
The classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10963666/ |
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| author | Ayesha Shafique Mohammad Siraj Benmao Cheng Saif A. Alsaif Tariq Sadad |
| author_facet | Ayesha Shafique Mohammad Siraj Benmao Cheng Saif A. Alsaif Tariq Sadad |
| author_sort | Ayesha Shafique |
| collection | DOAJ |
| description | The classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and those subjected to fertilizers in different concentration levels. This classification is fundamental to ensuring food quality and optimizing fertilizer input and precision agriculture practices. The dataset did not initially satisfy requirements owing to the small sample size, which rendered it difficult to train any robust generalizable model. A conditional GAN variation (CGAN) has been proposed for data augmentation. This approach effectively handled class imbalance by generating high-quality, category-specific synthetic images that enriched the dataset with realistic and diverse samples. For classification, the ConvNeXt architecture was employed, integrated with the Simple Attention Module (SimAM) to enhance feature refinement and extraction. The SimAM module allowed the model to focus on the most relevant areas of hyperspectral images while suppressing unimportant information, leading to a highly refined representation of features. The proposed classification model achieved an impressive accuracy of 99.75%, showcasing its exceptional ability to distinguish between different categories of apples, including untreated ones and those with varying fertilizer concentrations. This high level of accuracy underscores the model’s robustness and reliability in agricultural image analysis. |
| format | Article |
| id | doaj-art-2eb848a6007541edbdd79dafb7df55b9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2eb848a6007541edbdd79dafb7df55b92025-08-20T03:18:24ZengIEEEIEEE Access2169-35362025-01-0113664056641910.1109/ACCESS.2025.355994510963666Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated ApplesAyesha Shafique0https://orcid.org/0000-0001-7758-4102Mohammad Siraj1https://orcid.org/0000-0001-7805-0815Benmao Cheng2https://orcid.org/0000-0001-5270-3208Saif A. Alsaif3https://orcid.org/0000-0002-5442-0612Tariq Sadad4https://orcid.org/0000-0002-0078-7849Jiangsu Key (Construction) Laboratory of Intelligent IoT Technology and Applications in Universities, School of IoT Engineering, Wuxi Taihu University, Wuxi, ChinaDepartment of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaJiangsu Key (Construction) Laboratory of Intelligent IoT Technology and Applications in Universities, School of IoT Engineering, Wuxi Taihu University, Wuxi, ChinaDepartment of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, University of Engineering and Technology Mardan, Mardan, PakistanThe classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and those subjected to fertilizers in different concentration levels. This classification is fundamental to ensuring food quality and optimizing fertilizer input and precision agriculture practices. The dataset did not initially satisfy requirements owing to the small sample size, which rendered it difficult to train any robust generalizable model. A conditional GAN variation (CGAN) has been proposed for data augmentation. This approach effectively handled class imbalance by generating high-quality, category-specific synthetic images that enriched the dataset with realistic and diverse samples. For classification, the ConvNeXt architecture was employed, integrated with the Simple Attention Module (SimAM) to enhance feature refinement and extraction. The SimAM module allowed the model to focus on the most relevant areas of hyperspectral images while suppressing unimportant information, leading to a highly refined representation of features. The proposed classification model achieved an impressive accuracy of 99.75%, showcasing its exceptional ability to distinguish between different categories of apples, including untreated ones and those with varying fertilizer concentrations. This high level of accuracy underscores the model’s robustness and reliability in agricultural image analysis.https://ieeexplore.ieee.org/document/10963666/Hyperspectral imagingagricultural analysisConvNeXt architectureimage classificationprecision agriculture |
| spellingShingle | Ayesha Shafique Mohammad Siraj Benmao Cheng Saif A. Alsaif Tariq Sadad Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples IEEE Access Hyperspectral imaging agricultural analysis ConvNeXt architecture image classification precision agriculture |
| title | Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples |
| title_full | Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples |
| title_fullStr | Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples |
| title_full_unstemmed | Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples |
| title_short | Hyperspectral Imaging and Advanced Vision Transformers for Identifying Pure and Pesticide-Coated Apples |
| title_sort | hyperspectral imaging and advanced vision transformers for identifying pure and pesticide coated apples |
| topic | Hyperspectral imaging agricultural analysis ConvNeXt architecture image classification precision agriculture |
| url | https://ieeexplore.ieee.org/document/10963666/ |
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