COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis
Cotton leaf diseases are common agricultural issues and multifaceted threats to millions of farmers worldwide. This study aims to provide an up-to-date approach to diagnosing diseases of cotton leaves using advanced DL (Deep Learning) that is accessible and actionable by farmers, bridging the gap be...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000532 |
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| author | Md. Asraful Sharker Nirob Prayma Bishshash A K M Fazlul Kobir Siam |
| author_facet | Md. Asraful Sharker Nirob Prayma Bishshash A K M Fazlul Kobir Siam |
| author_sort | Md. Asraful Sharker Nirob |
| collection | DOAJ |
| description | Cotton leaf diseases are common agricultural issues and multifaceted threats to millions of farmers worldwide. This study aims to provide an up-to-date approach to diagnosing diseases of cotton leaves using advanced DL (Deep Learning) that is accessible and actionable by farmers, bridging the gap between technology and agricultural practices. The key to this study was the construction of a diverse dataset comprising 5722 high-resolution images collected from three independent sources and further enriched with expert guidance in annotation. Advanced preprocessing was done, like denoising, sharpening, and color balancing, to increase the quality of images, while augmentation increased generalization and reduced overfitting. In it, COLD-12 is proposed, which has achieved a promising result by incorporating Atrous Spatial Pyramid Pooling with Squeeze-and-Excitation blocks for improved channel attention in its multi-level feature extraction. The training accuracy of the model was 99.94 %, and the validation accuracy was 99.24 %, which indicates that the model was robust with very low validation loss. The inner workings of the COLD-12 model were interpreted by using explainable AI techniques such as Grad-CAM, Grad-CAM++, and Layer-CAM. It showed advantages compared to state-of-the-art models such as VGG16, VGG19, Xception, DenseNet121, and InceptionResNetV2.The impact of batch size, K-fold cross-validation, and preprocessing techniques was also explored, resulting in improved accuracy and interpretability. An interactive web tool was developed to bridge research and real-world applications, offering farmers a convenient assistant for diagnosing cotton leaf diseases, thus supporting sustainable farming initiatives. |
| format | Article |
| id | doaj-art-62cf35a5276b44db84ae5af33c0e4aec |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-62cf35a5276b44db84ae5af33c0e4aec2025-08-20T03:31:54ZengElsevierFranklin Open2773-18632025-06-011110026310.1016/j.fraope.2025.100263COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosisMd. Asraful Sharker Nirob0Prayma Bishshash1A K M Fazlul Kobir Siam2Corresponding author.; Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia 1216, Dhaka, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia 1216, Dhaka, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia 1216, Dhaka, BangladeshCotton leaf diseases are common agricultural issues and multifaceted threats to millions of farmers worldwide. This study aims to provide an up-to-date approach to diagnosing diseases of cotton leaves using advanced DL (Deep Learning) that is accessible and actionable by farmers, bridging the gap between technology and agricultural practices. The key to this study was the construction of a diverse dataset comprising 5722 high-resolution images collected from three independent sources and further enriched with expert guidance in annotation. Advanced preprocessing was done, like denoising, sharpening, and color balancing, to increase the quality of images, while augmentation increased generalization and reduced overfitting. In it, COLD-12 is proposed, which has achieved a promising result by incorporating Atrous Spatial Pyramid Pooling with Squeeze-and-Excitation blocks for improved channel attention in its multi-level feature extraction. The training accuracy of the model was 99.94 %, and the validation accuracy was 99.24 %, which indicates that the model was robust with very low validation loss. The inner workings of the COLD-12 model were interpreted by using explainable AI techniques such as Grad-CAM, Grad-CAM++, and Layer-CAM. It showed advantages compared to state-of-the-art models such as VGG16, VGG19, Xception, DenseNet121, and InceptionResNetV2.The impact of batch size, K-fold cross-validation, and preprocessing techniques was also explored, resulting in improved accuracy and interpretability. An interactive web tool was developed to bridge research and real-world applications, offering farmers a convenient assistant for diagnosing cotton leaf diseases, thus supporting sustainable farming initiatives.http://www.sciencedirect.com/science/article/pii/S2773186325000532COLD-12Deep-learningCNNPlant diseasesExplainable AIand Image processing |
| spellingShingle | Md. Asraful Sharker Nirob Prayma Bishshash A K M Fazlul Kobir Siam COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis Franklin Open COLD-12 Deep-learning CNN Plant diseases Explainable AI and Image processing |
| title | COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis |
| title_full | COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis |
| title_fullStr | COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis |
| title_full_unstemmed | COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis |
| title_short | COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis |
| title_sort | cold 12 a multi level feature extraction hybrid cnn model for accurate cotton disease diagnosis |
| topic | COLD-12 Deep-learning CNN Plant diseases Explainable AI and Image processing |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000532 |
| work_keys_str_mv | AT mdasrafulsharkernirob cold12amultilevelfeatureextractionhybridcnnmodelforaccuratecottondiseasediagnosis AT praymabishshash cold12amultilevelfeatureextractionhybridcnnmodelforaccuratecottondiseasediagnosis AT akmfazlulkobirsiam cold12amultilevelfeatureextractionhybridcnnmodelforaccuratecottondiseasediagnosis |