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Research Article
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Deep Learning-Based Damage Assessment in Cherry Leaves

Year 2024, Volume: 8 Issue: 2, 160 - 178, 31.12.2024

Hazel Bozcu Burakhan Çubukçu

https://doi.org/10.38088/jise.1455860

Abstract

This study aims to utilize deep learning methods for detecting diseases in cherry leaves to enhance agricultural productivity. While the detection of leaf diseases is currently performed by expert personnel, there may be a shortage of such experts, and the process can be time-consuming. Therefore, the primary objective of this study is to use deep learning-based disease detection applications to increase cherry production and enable early disease diagnosis. Additionally, the study investigates the impact of datasets on performance using two different datasets - one existing (PlantVillage Dataset) and one created for the study (Kozlu Dataset). Furthermore, the study examines the impact of hybrid architectures, combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in addition to transfer learning methods and classical CNNs. On the PlantVillage dataset, AlexNet, VGG-16, MobileNet-V2, Inception-V3, and CNN models were compared. Due to the low performance of AlexNet and the long training time of VGG-16, MobileNet-V2, Inception-V3, CNN, and two different CNN+RNN models were compared on the Kozlu dataset. According to the average results, the MobileNet-V2 model achieved the highest accuracy and F1-score in both datasets. The methods were observed to perform somewhat better on the PlantVillage dataset compared to the Kozlu dataset. Additionally, hybrid models (CNN+RNN) were found to achieve higher performance than the classical CNN model. These findings indicate promising outcomes for deep learning models in cherry leaf disease detection. The best results in the study were obtained by the MobileNet-V2 and the proposed CNN + LSTM models. In future studies, the reliability of this study can be increased by using more diverse datasets, and disease detection performance can be enhanced by using different deep learning methods, leading to reduced disease detection times.

Keywords

Transfer learning Convolutional neural networks Recurrent neural networks Long short term memory Deep learning

References

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  • [11] H. Bozcu, “Kozlu Dataset.” Accessed: Jun. 04, 2024. [Online]. Available: https://www.kaggle.com/datasets/hazelk26/kozlu-dataset
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  • [14] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint, 2015.
  • [15] “VGG16 - Convolutional Network for Classification and Detection.” Accessed: May 31, 2024. [Online]. Available: https://neurohive.io/en/popular-networks/vgg16/
  • [16] Q. Guan et al., “Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study,” Ann Transl Med, vol. 7, no. 14, pp. 307–307, Jul. 2019, doi: 10.21037/ATM.2019.06.29.
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  • [22] U. Barman, R. D. Choudhury, D. Sahu, and G. G. Barman, “Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease,” Comput Electron Agric, vol. 177, p. 105661, Oct. 2020, doi: 10.1016/J.COMPAG.2020.105661.
  • [23] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2012. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  • [24] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474.
  • [25] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations (ICLR 2015), Computational and Biological Learning Society, 2015, pp. 1–14.
  • [26] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA: IEEE Computer Society, Jun. 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
  • [27] H. Parlak and B. Çubukçu, “Vgg-19 Based Multiclass Model For Ovarian Cancer Classification From Histopathologic Images,” in Mas 19th International European Conference On Mathematics, Engineering, Natural & Medical Sciences, 2021, pp. 172–182.
  • [28] Z. B. G. Aydın and R. Şamlı, “A Comparison of Software Defect Prediction Metrics Using Data Mining Algorithms,” Journal of Innovative Science and Engineering, vol. 4, no. 1, pp. 11–21, Jun. 2020, doi: 10.38088/JISE.693098.
  • [29] Ş. Doğru and V. Altuntaş, “Prediction of Cancer in DNA Sequences Using Unsupervised Learning Methods,” Journal of Innovative Science and Engineering, vol. 7, no. 1, pp. 40–47, Jun. 2023, doi: 10.38088/JISE.1134816.
  • [30] T. Sulistyowati, P. PURWANTO, F. Alzami, and R. A. Pramunendar, “VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases,” Moneter: Jurnal Keuangan dan Perbankan, vol. 11, no. 1, pp. 41–53, Jan. 2023, doi: 10.32832/MONETER.V11I1.57.
  • [31] A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, Jan. 2022, doi: 10.1016/J.AIIA.2021.12.002.
  • [32] M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, “ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network,” Procedia Comput Sci, vol. 167, pp. 293–301, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.225.
  • [33] R. Thangaraj, P. Pandiyan, S. Anandamurugan, and S. Rajendar, “A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease,” Multimed Tools Appl, vol. 83, no. 7, pp. 18803–18827, Feb. 2024, doi: 10.1007/S11042-023-16347-0/TABLES/2.
  • [34] R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, and R. Menaka, “Attention embedded residual CNN for disease detection in tomato leaves,” Appl Soft Comput, vol. 86, p. 105933, Jan. 2020, doi: 10.1016/J.ASOC.2019.105933.
  • [35] T. Vijaykanth Reddy and K. Sashi Rekha, “Deep Leaf Disease Prediction Framework (DLDPF) with Transfer Learning for Automatic Leaf Disease Detection,” Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, pp. 1408–1415, Apr. 2021, doi: 10.1109/ICCMC51019.2021.9418245.
  • [36] H. Amin, A. Darwish, A. E. Hassanien, and M. Soliman, “End-to-End Deep Learning Model for Corn Leaf Disease Classification,” IEEE Access, vol. 10, pp. 31103–31115, 2022, doi: 10.1109/ACCESS.2022.3159678.
  • [37] R. Gao, R. Wang, L. Feng, Q. Li, and H. Wu, “Dual-branch, efficient, channel attention-based crop disease identification,” Comput Electron Agric, vol. 190, p. 106410, Nov. 2021, doi: 10.1016/J.COMPAG.2021.106410.
  • [38] P. S. Thakur, T. Sheorey, and A. Ojha, “VGG-ICNN: A Lightweight CNN model for crop disease identification,” Multimed Tools Appl, vol. 82, no. 1, pp. 497–520, Jan. 2023, doi: 10.1007/S11042-022-13144-Z/TABLES/10.
  • [39] E. Li, L. Wang, Q. Xie, R. Gao, Z. Su, and Y. Li, “A novel deep learning method for maize disease identification based on small sample-size and complex background datasets,” Ecol Inform, vol. 75, p. 102011, Jul. 2023, doi: 10.1016/J.ECOINF.2023.102011.

Year 2024, Volume: 8 Issue: 2, 160 - 178, 31.12.2024

Hazel Bozcu Burakhan Çubukçu

https://doi.org/10.38088/jise.1455860

Abstract

References

  • [1] G. F. S. Al Daban, “ Plant Disease Detection Using SVM Classification,” Altinbas University, İstanbul, 2019.
  • [2] Gıda Tarım ve Hayvancılık Bakanlığı, Kiraz Vişne Hastalık ve Zararlıları ile Mücadele, vol. 1. Ankara, 2016.
  • [3] Ş. Kurt, Bitki Fungal Hastalıkları, vol. 3. 2020.
  • [4] M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant Disease Detection and Classification by Deep Learning,” Plants, vol. 8, no. 11, 2019, doi: 10.3390/plants8110468.
  • [5] M. Sibiya and M. Sumbwanyambe, “A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks,” AgriEngineering, vol. 1, no. 1, pp. 119–131, 2019, doi: 10.3390/agriengineering1010009.
  • [6] P. Tejaswini, P. Singh, M. Ramchandani, Y. K. Rathore, and R. R. Janghel, “Rice Leaf Disease Classification Using Cnn,” IOP Conf Ser Earth Environ Sci, vol. 1032, no. 1, p. 12017, Jun. 2022, doi: 10.1088/1755-1315/1032/1/012017.
  • [7] E. C. Seyrek, “The Use of Machine and Deep Learning on Hyperspectral Image Classification Applications,” 2021. Accessed: Mar. 13, 2024. [Online]. Available: http://acikerisim.aku.edu.tr/xmlui/handle/11630/8546
  • [8] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front Plant Sci, vol. 7, 2016, doi: 10.3389/fpls.2016.01419.
  • [9] “PlantVillage Dataset.” Accessed: Jun. 04, 2024. [Online]. Available: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage
  • [10] F. Mohameth, C. Bingcai, K. A. Sada, F. Mohameth, C. Bingcai, and K. A. Sada, “Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village,” Journal of Computer and Communications, vol. 8, no. 6, pp. 10–22, Jun. 2020, doi: 10.4236/JCC.2020.86002.
  • [11] H. Bozcu, “Kozlu Dataset.” Accessed: Jun. 04, 2024. [Online]. Available: https://www.kaggle.com/datasets/hazelk26/kozlu-dataset
  • [12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2012. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  • [13] I. Naeem Oleiwi Al-Mahdi, “CNN googlenet and alexnet architecture deep learning for diabetic retinopathy image processing and classification,” İstanbul Gelişim Üniversitesi, İstanbul, 2023.
  • [14] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint, 2015.
  • [15] “VGG16 - Convolutional Network for Classification and Detection.” Accessed: May 31, 2024. [Online]. Available: https://neurohive.io/en/popular-networks/vgg16/
  • [16] Q. Guan et al., “Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study,” Ann Transl Med, vol. 7, no. 14, pp. 307–307, Jul. 2019, doi: 10.21037/ATM.2019.06.29.
  • [17] X. Xia, C. Xu, and B. Nan, “Inception-v3 for flower classification,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017, pp. 783–787. doi: 10.1109/ICIVC.2017.7984661.
  • [18] S. Kumar, R. Ratan, and J. V. Desai, “Cotton Disease Detection Using TensorFlow Machine Learning Technique,” Advances in Multimedia, vol. 2022, 2022, doi: 10.1155/2022/1812025.
  • [19] L. Ali, F. Alnajjar, H. Al Jassmi, M. Gochoo, W. Khan, and M. A. Serhani, “Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures,” Sensors 2021, Vol. 21, Page 1688, vol. 21, no. 5, p. 1688, Mar. 2021, doi: 10.3390/S21051688.
  • [20] S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognit, vol. 71, pp. 1–13, Nov. 2017, doi: 10.1016/J.PATCOG.2017.05.015.
  • [21] A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv e-prints, p. arXiv:1704.04861, Apr. 2017, doi: 10.48550/arXiv.1704.04861.
  • [22] U. Barman, R. D. Choudhury, D. Sahu, and G. G. Barman, “Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease,” Comput Electron Agric, vol. 177, p. 105661, Oct. 2020, doi: 10.1016/J.COMPAG.2020.105661.
  • [23] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2012. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  • [24] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474.
  • [25] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations (ICLR 2015), Computational and Biological Learning Society, 2015, pp. 1–14.
  • [26] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA: IEEE Computer Society, Jun. 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
  • [27] H. Parlak and B. Çubukçu, “Vgg-19 Based Multiclass Model For Ovarian Cancer Classification From Histopathologic Images,” in Mas 19th International European Conference On Mathematics, Engineering, Natural & Medical Sciences, 2021, pp. 172–182.
  • [28] Z. B. G. Aydın and R. Şamlı, “A Comparison of Software Defect Prediction Metrics Using Data Mining Algorithms,” Journal of Innovative Science and Engineering, vol. 4, no. 1, pp. 11–21, Jun. 2020, doi: 10.38088/JISE.693098.
  • [29] Ş. Doğru and V. Altuntaş, “Prediction of Cancer in DNA Sequences Using Unsupervised Learning Methods,” Journal of Innovative Science and Engineering, vol. 7, no. 1, pp. 40–47, Jun. 2023, doi: 10.38088/JISE.1134816.
  • [30] T. Sulistyowati, P. PURWANTO, F. Alzami, and R. A. Pramunendar, “VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases,” Moneter: Jurnal Keuangan dan Perbankan, vol. 11, no. 1, pp. 41–53, Jan. 2023, doi: 10.32832/MONETER.V11I1.57.
  • [31] A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, Jan. 2022, doi: 10.1016/J.AIIA.2021.12.002.
  • [32] M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, “ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network,” Procedia Comput Sci, vol. 167, pp. 293–301, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.225.
  • [33] R. Thangaraj, P. Pandiyan, S. Anandamurugan, and S. Rajendar, “A deep convolution neural network model based on feature concatenation approach for classification of tomato leaf disease,” Multimed Tools Appl, vol. 83, no. 7, pp. 18803–18827, Feb. 2024, doi: 10.1007/S11042-023-16347-0/TABLES/2.
  • [34] R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, and R. Menaka, “Attention embedded residual CNN for disease detection in tomato leaves,” Appl Soft Comput, vol. 86, p. 105933, Jan. 2020, doi: 10.1016/J.ASOC.2019.105933.
  • [35] T. Vijaykanth Reddy and K. Sashi Rekha, “Deep Leaf Disease Prediction Framework (DLDPF) with Transfer Learning for Automatic Leaf Disease Detection,” Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, pp. 1408–1415, Apr. 2021, doi: 10.1109/ICCMC51019.2021.9418245.
  • [36] H. Amin, A. Darwish, A. E. Hassanien, and M. Soliman, “End-to-End Deep Learning Model for Corn Leaf Disease Classification,” IEEE Access, vol. 10, pp. 31103–31115, 2022, doi: 10.1109/ACCESS.2022.3159678.
  • [37] R. Gao, R. Wang, L. Feng, Q. Li, and H. Wu, “Dual-branch, efficient, channel attention-based crop disease identification,” Comput Electron Agric, vol. 190, p. 106410, Nov. 2021, doi: 10.1016/J.COMPAG.2021.106410.
  • [38] P. S. Thakur, T. Sheorey, and A. Ojha, “VGG-ICNN: A Lightweight CNN model for crop disease identification,” Multimed Tools Appl, vol. 82, no. 1, pp. 497–520, Jan. 2023, doi: 10.1007/S11042-022-13144-Z/TABLES/10.
  • [39] E. Li, L. Wang, Q. Xie, R. Gao, Z. Su, and Y. Li, “A novel deep learning method for maize disease identification based on small sample-size and complex background datasets,” Ecol Inform, vol. 75, p. 102011, Jul. 2023, doi: 10.1016/J.ECOINF.2023.102011.
There are 39 citations in total.

Details

Primary Language English
Subjects Deep Learning
Journal Section Research Articles
Authors

Hazel Bozcu 0009-0006-7001-9120

Burakhan Çubukçu 0000-0003-0480-1254

Early Pub Date December 11, 2024
Publication Date December 31, 2024
Submission Date March 20, 2024
Acceptance Date June 15, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Bozcu, H., & Çubukçu, B. (2024). Deep Learning-Based Damage Assessment in Cherry Leaves. Journal of Innovative Science and Engineering, 8(2), 160-178. https://doi.org/10.38088/jise.1455860
AMA Bozcu H, Çubukçu B. Deep Learning-Based Damage Assessment in Cherry Leaves. JISE. December 2024;8(2):160-178. doi:10.38088/jise.1455860
Chicago Bozcu, Hazel, and Burakhan Çubukçu. “Deep Learning-Based Damage Assessment in Cherry Leaves”. Journal of Innovative Science and Engineering 8, no. 2 (December 2024): 160-78. https://doi.org/10.38088/jise.1455860.
EndNote Bozcu H, Çubukçu B (December 1, 2024) Deep Learning-Based Damage Assessment in Cherry Leaves. Journal of Innovative Science and Engineering 8 2 160–178.
IEEE H. Bozcu and B. Çubukçu, “Deep Learning-Based Damage Assessment in Cherry Leaves”, JISE, vol. 8, no. 2, pp. 160–178, 2024, doi: 10.38088/jise.1455860.
ISNAD Bozcu, Hazel - Çubukçu, Burakhan. “Deep Learning-Based Damage Assessment in Cherry Leaves”. Journal of Innovative Science and Engineering 8/2 (December2024), 160-178. https://doi.org/10.38088/jise.1455860.
JAMA Bozcu H, Çubukçu B. Deep Learning-Based Damage Assessment in Cherry Leaves. JISE. 2024;8:160–178.
MLA Bozcu, Hazel and Burakhan Çubukçu. “Deep Learning-Based Damage Assessment in Cherry Leaves”. Journal of Innovative Science and Engineering, vol. 8, no. 2, 2024, pp. 160-78, doi:10.38088/jise.1455860.
Vancouver Bozcu H, Çubukçu B. Deep Learning-Based Damage Assessment in Cherry Leaves. JISE. 2024;8(2):160-78.
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