Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 13 Sayı: 1, 247 - 258, 24.03.2024
https://doi.org/10.17798/bitlisfen.1380995

Öz

Kaynakça

  • [1] M. Barrio-Conde, M. A. Zanella, J. M. Aguiar-Perez, R. Ruiz-Gonzalez, and J. Gomez-Gil, ‘A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties’, Sensors, vol. 23, no. 5, p. 2471, Feb. 2023, doi: 10.3390/s23052471.
  • [2] D. Banerjee, V. Kukreja, S. Vats, V. Jain, and B. Goyal, ‘AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines’, in 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India: IEEE, Jul. 2023, pp. 722–726. doi: 10.1109/ICESC57686.2023.10193473.
  • [3] S. Khalifani, R. Darvishzadeh, N. Azad, and R. Seyed Rahmani, ‘Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models’, Industrial Crops and Products, vol. 189, p. 115762, Dec. 2022, doi: 10.1016/j.indcrop.2022.115762.
  • [4] P. Ghosh, A. K. Mondal, S. Chatterjee, M. Masud, H. Meshref, and A. K. Bairagi, ‘Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI’, Mathematics, vol. 11, no. 10, p. 2241, May 2023, doi: 10.3390/math11102241.
  • [5] A. Sirohi and A. Malik, ‘A Hybrid Model for the Classification of Sunflower Diseases Using Deep Learning’, in 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom: IEEE, Apr. 2021, pp. 58–62. doi: 10.1109/ICIEM51511.2021.9445342.
  • [6] S. Chen, F. Lv, and P. Huo, ‘Improved detection of yolov4 sunflower leaf diseases’, in 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China: IEEE, Aug. 2021, pp. 56–59. doi: 10.1109/ISCEIC53685.2021.00019.
  • [7] U. Sara, A. Rajbongshi, R. Shakil, B. Akter, S. Sazzad, and M. S. Uddin, ‘An extensive sunflower dataset representation for successful identification and classification of sunflower diseases’, Data in Brief, vol. 42, p. 108043, Jun. 2022, doi: 10.1016/j.dib.2022.108043.
  • [8] R. G. Dawod and C. Dobre, ‘Classification of Sunflower Foliar Diseases Using Convolutional Neural Network’, in 2021 23rd International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania: IEEE, May 2021, pp. 476–481. doi: 10.1109/CSCS52396.2021.00084.
  • [9] A. Rajbongshi, A. A. Biswas, J. Biswas, R. Shakil, B. Akter, and M. R. Barman, ‘Sunflower Diseases Recognition Using Computer Vision-Based Approach’, in 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India: IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/R10-HTC53172.2021.9641588.
  • [10] A. Malik et al., ‘Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach’, Journal of Food Quality, vol. 2022, pp. 1–12, Apr. 2022, doi: 10.1155/2022/9211700.
  • [11] V. Singh, ‘Sunflower leaf diseases detection using image segmentation based on particle swarm optimization’, Artificial Intelligence in Agriculture, vol. 3, pp. 62–68, Sep. 2019, doi: 10.1016/j.aiia.2019.09.002.
  • [12] Aditya Rajbongshi, ‘Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning’. Mendeley, Jan. 18, 2022. doi: 10.17632/B83HMRZTH8.1.
  • [13] Ö. Arslan and S. A. Uymaz, ‘Classification of Invoice Images by Using Convolutional Neural Networks’, Journal of Advanced Research in Natural and Applied Sciences, vol. 8, no. 1, pp. 8–25, Mar. 2022, doi: 10.28979/jarnas.953634.
  • [14] B. Gencturk et al., ‘Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models’, Eur Food Res Technol, Sep. 2023, doi: 10.1007/s00217-023-04369-9.
  • [15] K. He, X. Zhang, S. Ren, and J. Sun, ‘Deep Residual Learning for Image Recognition’, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA: IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
  • [16] E. E. Kilinç, F. Aka, and S. Metlek, ‘3BResNet: A Novel Residual Block-Based ResNet Model Approach for COVID19 Detection’, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 3, pp. 925–940, Sep. 2023, doi: 10.17798/bitlisfen.1346730.
  • [17] K. Lin et al., ‘Applying a deep residual network coupling with transfer learning for recyclable waste sorting’, Environ Sci Pollut Res, vol. 29, no. 60, pp. 91081–91095, Dec. 2022, doi: 10.1007/s11356-022-22167-w.
  • [18] J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, ‘Squeeze-and-Excitation Networks’, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, Aug. 2020, doi: 10.1109/TPAMI.2019.2913372.
  • [19] S. Chen, T. Wang, Z. Huang, and X. Hou, ‘Detection method of Golden Chip-Free Hardware Trojan based on the combination of ResNeXt structure and attention mechanism’, Computers & Security, vol. 134, p. 103428, Nov. 2023, doi: 10.1016/j.cose.2023.103428.
  • [20] S. S. Chaturvedi, J. V. Tembhurne, and T. Diwan, ‘A multi-class skin Cancer classification using deep convolutional neural networks’, Multimed Tools Appl, vol. 79, no. 39–40, pp. 28477–28498, Oct. 2020, doi: 10.1007/s11042-020-09388-2.
  • [21] A. Ramana Kumari, S. Nagaraja Rao, and P. Ramana Reddy, ‘Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN’, Biomedical Signal Processing and Control, vol. 78, p. 103961, Sep. 2022, doi: 10.1016/j.bspc.2022.103961.
  • [22] I. Naseer, S. Akram, T. Masood, A. Jaffar, M. A. Khan, and A. Mosavi, ‘Performance Analysis of State-of-the-Art CNN Architectures for LUNA16’, Sensors, vol. 22, no. 12, p. 4426, Jun. 2022, doi: 10.3390/s22124426.
  • [23] S. F. Ahmed et al., ‘Deep learning modelling techniques: current progress, applications, advantages, and challenges’, Artif Intell Rev, vol. 56, no. 11, pp. 13521–13617, Nov. 2023, doi: 10.1007/s10462-023-10466-8.

Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks

Yıl 2024, Cilt: 13 Sayı: 1, 247 - 258, 24.03.2024
https://doi.org/10.17798/bitlisfen.1380995

Öz

Diseases in agricultural plants are one of the most important problems of agricultural production. These diseases cause decreases in production and this poses a serious problem for food safety. One of the agricultural products is sunflower. Helianthus annuus, generally known as sunflower, is an agricultural plant with high economic value grown due to its drought-resistant and oil seeds. In this study, it is aimed to classify the diseases seen in sunflower leaves and flowers by applying deep learning models. First of all, it was classified with ResNet101 and ResNext101, which are pre-trained CNN models, and then it was classified by adding squeeze and excitation blocks to these networks and the results were compared. In the study, a data set containing gray mold, downy mildew, and leaf scars diseases affecting the sunflower crop was used. In our study, original Resnet101, SE-Resnet101, ResNext101, and SE-ResNext101 deep-learning models were used to classify sunflower diseases. For the original images, the classification accuracy of 91.48% with Resnet101, 92.55% with SE-Resnet101, 92.55% with ResNext101, and 94.68% with SE-ResNext101 was achieved. The same models were also suitable for augmented images and classification accuracies of Resnet101 99.20%, SE-Resnet101 99.47%, ResNext101 98.94%, and SE-ResNext101 99.84% were achieved. The study revealed a comparative analysis of deep learning models for the classification of some diseases in the Sunflower plant. In the analysis, it was seen that SE blocks increased the classification performance for this dataset. Application of these models to real-world agricultural scenarios holds promise for early disease detection and response and may help reduce potential crop losses.

Etik Beyan

The study is complied with research and publication ethics.

Kaynakça

  • [1] M. Barrio-Conde, M. A. Zanella, J. M. Aguiar-Perez, R. Ruiz-Gonzalez, and J. Gomez-Gil, ‘A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties’, Sensors, vol. 23, no. 5, p. 2471, Feb. 2023, doi: 10.3390/s23052471.
  • [2] D. Banerjee, V. Kukreja, S. Vats, V. Jain, and B. Goyal, ‘AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines’, in 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India: IEEE, Jul. 2023, pp. 722–726. doi: 10.1109/ICESC57686.2023.10193473.
  • [3] S. Khalifani, R. Darvishzadeh, N. Azad, and R. Seyed Rahmani, ‘Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models’, Industrial Crops and Products, vol. 189, p. 115762, Dec. 2022, doi: 10.1016/j.indcrop.2022.115762.
  • [4] P. Ghosh, A. K. Mondal, S. Chatterjee, M. Masud, H. Meshref, and A. K. Bairagi, ‘Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI’, Mathematics, vol. 11, no. 10, p. 2241, May 2023, doi: 10.3390/math11102241.
  • [5] A. Sirohi and A. Malik, ‘A Hybrid Model for the Classification of Sunflower Diseases Using Deep Learning’, in 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom: IEEE, Apr. 2021, pp. 58–62. doi: 10.1109/ICIEM51511.2021.9445342.
  • [6] S. Chen, F. Lv, and P. Huo, ‘Improved detection of yolov4 sunflower leaf diseases’, in 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China: IEEE, Aug. 2021, pp. 56–59. doi: 10.1109/ISCEIC53685.2021.00019.
  • [7] U. Sara, A. Rajbongshi, R. Shakil, B. Akter, S. Sazzad, and M. S. Uddin, ‘An extensive sunflower dataset representation for successful identification and classification of sunflower diseases’, Data in Brief, vol. 42, p. 108043, Jun. 2022, doi: 10.1016/j.dib.2022.108043.
  • [8] R. G. Dawod and C. Dobre, ‘Classification of Sunflower Foliar Diseases Using Convolutional Neural Network’, in 2021 23rd International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania: IEEE, May 2021, pp. 476–481. doi: 10.1109/CSCS52396.2021.00084.
  • [9] A. Rajbongshi, A. A. Biswas, J. Biswas, R. Shakil, B. Akter, and M. R. Barman, ‘Sunflower Diseases Recognition Using Computer Vision-Based Approach’, in 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India: IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/R10-HTC53172.2021.9641588.
  • [10] A. Malik et al., ‘Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach’, Journal of Food Quality, vol. 2022, pp. 1–12, Apr. 2022, doi: 10.1155/2022/9211700.
  • [11] V. Singh, ‘Sunflower leaf diseases detection using image segmentation based on particle swarm optimization’, Artificial Intelligence in Agriculture, vol. 3, pp. 62–68, Sep. 2019, doi: 10.1016/j.aiia.2019.09.002.
  • [12] Aditya Rajbongshi, ‘Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning’. Mendeley, Jan. 18, 2022. doi: 10.17632/B83HMRZTH8.1.
  • [13] Ö. Arslan and S. A. Uymaz, ‘Classification of Invoice Images by Using Convolutional Neural Networks’, Journal of Advanced Research in Natural and Applied Sciences, vol. 8, no. 1, pp. 8–25, Mar. 2022, doi: 10.28979/jarnas.953634.
  • [14] B. Gencturk et al., ‘Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models’, Eur Food Res Technol, Sep. 2023, doi: 10.1007/s00217-023-04369-9.
  • [15] K. He, X. Zhang, S. Ren, and J. Sun, ‘Deep Residual Learning for Image Recognition’, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA: IEEE, Jun. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
  • [16] E. E. Kilinç, F. Aka, and S. Metlek, ‘3BResNet: A Novel Residual Block-Based ResNet Model Approach for COVID19 Detection’, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 3, pp. 925–940, Sep. 2023, doi: 10.17798/bitlisfen.1346730.
  • [17] K. Lin et al., ‘Applying a deep residual network coupling with transfer learning for recyclable waste sorting’, Environ Sci Pollut Res, vol. 29, no. 60, pp. 91081–91095, Dec. 2022, doi: 10.1007/s11356-022-22167-w.
  • [18] J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, ‘Squeeze-and-Excitation Networks’, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, Aug. 2020, doi: 10.1109/TPAMI.2019.2913372.
  • [19] S. Chen, T. Wang, Z. Huang, and X. Hou, ‘Detection method of Golden Chip-Free Hardware Trojan based on the combination of ResNeXt structure and attention mechanism’, Computers & Security, vol. 134, p. 103428, Nov. 2023, doi: 10.1016/j.cose.2023.103428.
  • [20] S. S. Chaturvedi, J. V. Tembhurne, and T. Diwan, ‘A multi-class skin Cancer classification using deep convolutional neural networks’, Multimed Tools Appl, vol. 79, no. 39–40, pp. 28477–28498, Oct. 2020, doi: 10.1007/s11042-020-09388-2.
  • [21] A. Ramana Kumari, S. Nagaraja Rao, and P. Ramana Reddy, ‘Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN’, Biomedical Signal Processing and Control, vol. 78, p. 103961, Sep. 2022, doi: 10.1016/j.bspc.2022.103961.
  • [22] I. Naseer, S. Akram, T. Masood, A. Jaffar, M. A. Khan, and A. Mosavi, ‘Performance Analysis of State-of-the-Art CNN Architectures for LUNA16’, Sensors, vol. 22, no. 12, p. 4426, Jun. 2022, doi: 10.3390/s22124426.
  • [23] S. F. Ahmed et al., ‘Deep learning modelling techniques: current progress, applications, advantages, and challenges’, Artif Intell Rev, vol. 56, no. 11, pp. 13521–13617, Nov. 2023, doi: 10.1007/s10462-023-10466-8.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Yavuz Ünal 0000-0002-3007-679X

Muhammet Nuri Dudak 0000-0003-2695-8447

Erken Görünüm Tarihi 21 Mart 2024
Yayımlanma Tarihi 24 Mart 2024
Gönderilme Tarihi 25 Ekim 2023
Kabul Tarihi 3 Ocak 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 1

Kaynak Göster

IEEE Y. Ünal ve M. N. Dudak, “Deep Learning Approaches for Sunflower Disease Classification: A Study of Convolutional Neural Networks with Squeeze and Excitation Attention Blocks”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 13, sy. 1, ss. 247–258, 2024, doi: 10.17798/bitlisfen.1380995.



Bitlis Eren Üniversitesi
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E-posta: fbe@beu.edu.tr