Research Article
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Year 2024, , 61 - 76, 30.04.2024

Muhammet Çakmak

https://doi.org/10.35377/saucis...1418505

Abstract

References

  • [1] S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Comput Electron Agric, vol. 72, no. 1, pp. 1–13, Jun. 2010, doi: 10.1016/j.compag.2010.02.007.
  • [2] S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2003–2010. doi: 10.1016/j.procs.2020.03.236.
  • [3] D. Ozdemir and M. S. Kunduraci, “Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application,” IEEE Access, vol. 10, pp. 35675–35684, 2022, doi: 10.1109/ACCESS.2022.3163380.
  • [4] M. AKIN, A. DAĞDELEN, R. N. EĞİNME, and D. ÖZDEMİR, “Doğada Kendiliğinden Yetişen Mantar Türlerinin Derin Öğrenme İle Tespiti,” Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, Dec. 2023, doi: 10.53608/estudambilisim.1319221.
  • [5] D. ÖZDEMİR and N. N. ARSLAN, “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 10, no. 2, pp. 628–640, Apr. 2022, doi: 10.29130/dubited.976118.
  • [6] E. Şahin, D. Özdemir, and H. Temurtaş, “Multi-objective optimization of ViT architecture for efficient brain tumor classification,” Biomed Signal Process Control, vol. 91, May 2024, doi: 10.1016/j.bspc.2023.105938.
  • [7] N. N. Arslan, D. Ozdemir, and H. Temurtas, “ECG heartbeats classification with dilated convolutional autoencoder,” Signal Image Video Process, Feb. 2023, doi: 10.1007/s11760-023-02737-2.
  • [8] B. Guler Ayyildiz, R. Karakis, B. Terzioglu, and D. Ozdemir, “Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages,” Dentomaxillofac Radiol, vol. 53, no. 1, pp. 32–42, Jan. 2024, doi: 10.1093/dmfr/twad003.
  • [9] D. Ozdemir and M. Emin UGUR, “Deniz Yildizlari Vocational and Technical Anatolian High School Ministry of National Education Kocaeli.”
  • [10] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput Electron Agric, vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/j.compag.2018.01.009.
  • [11] F. Jiang et al., “Artificial intelligence in healthcare: Past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4. BMJ Publishing Group, pp. 230–243, Dec. 01, 2017. doi: 10.1136/svn-2017-000101.
  • [12] P. Dong, K. Li, M. Wang, F. Li, W. Guo, and H. Si, “Maize Leaf Compound Disease Recognition Based on Attention Mechanism,” Agriculture (Switzerland), vol. 14, no. 1, Jan. 2024, doi: 10.3390/agriculture14010074.
  • [13] R. Ahila Priyadharshini, S. Arivazhagan, M. Arun, and A. Mirnalini, “Maize leaf disease classification using deep convolutional neural networks,” Neural Comput Appl, vol. 31, no. 12, 2019, doi: 10.1007/s00521-019-04228-3.
  • [14] V. Sharma, A. K. Tripathi, P. Daga, M. Nidhi, and H. Mittal, “ClGanNet: A novel method for maize leaf disease identification using ClGan and deep CNN,” Signal Process Image Commun, vol. 120, Jan. 2024, doi: 10.1016/j.image.2023.117074.
  • [15] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, “Fast and Accurate Detection and Classification of Plant Diseases,” 2011.
  • [16] P. Revathi and M. Hemalatha, “Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection,” in 2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012, 2012. doi: 10.1109/ICCCNT.2012.6395903.
  • [17] U. Mokhtar, M. A. S. Ali, A. E. Hassanien, and H. Hefny, “Identifying two of tomatoes leaf viruses using support vector machine,” in Advances in Intelligent Systems and Computing, Springer Verlag, 2015, pp. 771–782. doi: 10.1007/978-81-322-2250-7_77.
  • [18] X. E. Pantazi, D. Moshou, A. A. Tamouridou, and S. Kasderidis, “Leaf disease recognition in vine plants based on local binary patterns and one class support vector machines,” in IFIP Advances in Information and Communication Technology, Springer New York LLC, 2016, pp. 319–327. doi: 10.1007/978-3-319-44944-9_27.
  • [19] A. Johannes et al., “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case,” Comput Electron Agric, vol. 138, pp. 200–209, Jun. 2017, doi: 10.1016/j.compag.2017.04.013.
  • [20] J. Chen, H. Yin, and D. Zhang, “A self-adaptive classification method for plant disease detection using GMDH-Logistic model,” Sustainable Computing: Informatics and Systems, vol. 28, Dec. 2020, doi: 10.1016/j.suscom.2020.100415.
  • [21] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015. doi: 10.1038/nature14539.
  • [22] H. C. Altunay and Z. Albayrak, “A hybrid CNN + LSTMbased intrusion detection system for industrial IoT networks,” Engineering Science and Technology, an International Journal, vol. 38, Feb. 2023, doi: 10.1016/j.jestch.2022.101322.
  • [23] M. Çakmak and Z. Albayrak, “AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks,” Mobile Networks and Applications, vol. 27, no. 5, 2022, doi: 10.1007/s11036-022-02011-8.
  • [24] R. İncir and F. Bozkurt, “A study on effective data preprocessing and augmentation method in diabetic retinopathy classification using pre-trained deep learning approaches,” Multimed Tools Appl, vol. 83, no. 4, pp. 12185–12208, Jan. 2023, doi: 10.1007/S11042-023-15754-7/TABLES/7.
  • [25] D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” 2017, doi: 10.1146/annurev-bioeng-071516.
  • [26] S. NAHZAT, F. BOZKURT, and M. YAĞANOĞLU, “White Blood Cell Classification Using Convolutional Neural Network,” Journal of Scientific Technology and Engineering Research, 2022, doi: 10.53525/jster.1018213.
  • [27] F. Bozkurt, “A deep and handcrafted features-based framework for diagnosis of COVID-19 from chest x-ray images,” Concurr Comput, vol. 34, no. 5, 2022, doi: 10.1002/cpe.6725.
  • [28] A. Ahmad, D. Saraswat, and A. El Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agricultural Technology, vol. 3. 2023. doi: 10.1016/j.atech.2022.100083.
  • [29] J. Chen, A. Zeb, Y. A. Nanehkaran, and D. Zhang, “Stacking ensemble model of deep learning for plant disease recognition,” J Ambient Intell Humaniz Comput, vol. 14, no. 9, 2023, doi: 10.1007/s12652-022-04334-6.
  • [30] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput Electron Agric, vol. 173, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
  • [31] M. Nawaz, T. Nazir, A. Javed, S. Tawfik Amin, F. Jeribi, and A. Tahir, “CoffeeNet: A deep learning approach for coffee plant leaves diseases recognition,” Expert Syst Appl, vol. 237, 2024, doi: 10.1016/j.eswa.2023.121481.
  • [32] E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput Electron Agric, vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
  • [33] G. Geetharamani and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers and Electrical Engineering, vol. 76, pp. 323–338, Jun. 2019, doi: 10.1016/j.compeleceng.2019.04.011.
  • [34] B. Chang, Y. Wang, X. Zhao, G. Li, and P. Yuan, “A general-purpose edge-feature guidance module to enhance vision transformers for plant disease identification[Formula presented],” Expert Syst Appl, vol. 237, 2024, doi: 10.1016/j.eswa.2023.121638.
  • [35] A. L. Latifah, Lembaga Ilmu Pengetahuan Indonesia. Research Center for Informatics, Institute of Electrical and Electronics Engineers. Indonesia Section, and Institute of Electrical and Electronics Engineers, 2018 International Conference on Computer, Control, Informatics and its Applications : “Recent Challenges in Machine Learning for Computing Applications” : proceedings : November 1st-2nd, 2018, Tangerang, Indonesia.
  • [36] S. M. Hassan, M. Jasinski, Z. Leonowicz, E. Jasinska, and A. K. Maji, “Plant disease identification using shallow convolutional neural network,” Agronomy, vol. 11, no. 12, Dec. 2021, doi: 10.3390/agronomy11122388.
  • [37] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol Inform, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
  • [38] A. M. Fayyaz et al., “Leaf blights detection and classification in large scale applications,” Intelligent Automation and Soft Computing, vol. 31, no. 1, pp. 507–522, 2022, doi: 10.32604/IASC.2022.016392.
  • [39] A. Elaraby, W. Hamdy, and M. Alruwaili, “Optimization of deep learning model for plant disease detection using particle swarm optimizer,” Computers, Materials and Continua, vol. 71, no. 2, pp. 4019–4031, 2022, doi: 10.32604/cmc.2022.022161.
  • [40] A. N. Özalp and Z. Albayrak, “Detecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithms,” 2023.
  • [41] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2018, pp. 270–279. doi: 10.1007/978-3-030-01424-7_27.
  • [42] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition.” [Online]. Available: http://image-net.org/challenges/LSVRC/2015/
  • [43] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int J Comput Vis, vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/s11263-015-0816-y.
  • [44] K. Simonyan and A. Zisserman, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION,” 2015. [Online]. Available: http://www.robots.ox.ac.uk/
  • [45] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks.” [Online]. Available: https://github.com/liuzhuang13/DenseNet.
  • [46] A. Sardar, A. Issa, and Z. Albayrak, “DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM,” 2023.
  • [47] Q. Ji, J. Huang, W. He, and Y. Sun, “Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images,” Algorithms, vol. 12, no. 3, 2019, doi: 10.3390/a12030051.
  • [48] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.00567
  • [49] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.00567
  • [50] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.04381
  • [51] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available: http://arxiv.org/abs/1905.11946

Automatic Maize Leaf Disease Recognition Using Deep Learning

Year 2024, , 61 - 76, 30.04.2024

Muhammet Çakmak

https://doi.org/10.35377/saucis...1418505

Abstract

Maize leaf diseases exhibit visible symptoms and are currently diagnosed by expert pathologists through personal observation, but the slow manual detection methods and pathologist's skill influence make it challenging to identify diseases in maize leaves. Therefore, computer-aided diagnostic systems offer a promising solution for disease detection issues. While traditional machine learning methods require perfect manual feature extraction for image classification, deep learning networks extract image features autonomously and function without pre-processing. This study proposes using the EfficientNet deep learning model for the classification of maize leaf diseases and compares it with another established deep learning model. The maize leaf disease dataset was used to train all models, with 4188 images for the original dataset and 6176 images for the augmented dataset. The EfficientNet B6 model achieved 98.10% accuracy on the original dataset, while the EfficientNet B3 model achieved the highest accuracy of 99.66% on the augmented dataset.

Keywords

Deep Learning Transfer Learning Plant Disease Classification

References

  • [1] S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Comput Electron Agric, vol. 72, no. 1, pp. 1–13, Jun. 2010, doi: 10.1016/j.compag.2010.02.007.
  • [2] S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2003–2010. doi: 10.1016/j.procs.2020.03.236.
  • [3] D. Ozdemir and M. S. Kunduraci, “Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application,” IEEE Access, vol. 10, pp. 35675–35684, 2022, doi: 10.1109/ACCESS.2022.3163380.
  • [4] M. AKIN, A. DAĞDELEN, R. N. EĞİNME, and D. ÖZDEMİR, “Doğada Kendiliğinden Yetişen Mantar Türlerinin Derin Öğrenme İle Tespiti,” Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, Dec. 2023, doi: 10.53608/estudambilisim.1319221.
  • [5] D. ÖZDEMİR and N. N. ARSLAN, “Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 10, no. 2, pp. 628–640, Apr. 2022, doi: 10.29130/dubited.976118.
  • [6] E. Şahin, D. Özdemir, and H. Temurtaş, “Multi-objective optimization of ViT architecture for efficient brain tumor classification,” Biomed Signal Process Control, vol. 91, May 2024, doi: 10.1016/j.bspc.2023.105938.
  • [7] N. N. Arslan, D. Ozdemir, and H. Temurtas, “ECG heartbeats classification with dilated convolutional autoencoder,” Signal Image Video Process, Feb. 2023, doi: 10.1007/s11760-023-02737-2.
  • [8] B. Guler Ayyildiz, R. Karakis, B. Terzioglu, and D. Ozdemir, “Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages,” Dentomaxillofac Radiol, vol. 53, no. 1, pp. 32–42, Jan. 2024, doi: 10.1093/dmfr/twad003.
  • [9] D. Ozdemir and M. Emin UGUR, “Deniz Yildizlari Vocational and Technical Anatolian High School Ministry of National Education Kocaeli.”
  • [10] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput Electron Agric, vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/j.compag.2018.01.009.
  • [11] F. Jiang et al., “Artificial intelligence in healthcare: Past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4. BMJ Publishing Group, pp. 230–243, Dec. 01, 2017. doi: 10.1136/svn-2017-000101.
  • [12] P. Dong, K. Li, M. Wang, F. Li, W. Guo, and H. Si, “Maize Leaf Compound Disease Recognition Based on Attention Mechanism,” Agriculture (Switzerland), vol. 14, no. 1, Jan. 2024, doi: 10.3390/agriculture14010074.
  • [13] R. Ahila Priyadharshini, S. Arivazhagan, M. Arun, and A. Mirnalini, “Maize leaf disease classification using deep convolutional neural networks,” Neural Comput Appl, vol. 31, no. 12, 2019, doi: 10.1007/s00521-019-04228-3.
  • [14] V. Sharma, A. K. Tripathi, P. Daga, M. Nidhi, and H. Mittal, “ClGanNet: A novel method for maize leaf disease identification using ClGan and deep CNN,” Signal Process Image Commun, vol. 120, Jan. 2024, doi: 10.1016/j.image.2023.117074.
  • [15] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, “Fast and Accurate Detection and Classification of Plant Diseases,” 2011.
  • [16] P. Revathi and M. Hemalatha, “Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection,” in 2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012, 2012. doi: 10.1109/ICCCNT.2012.6395903.
  • [17] U. Mokhtar, M. A. S. Ali, A. E. Hassanien, and H. Hefny, “Identifying two of tomatoes leaf viruses using support vector machine,” in Advances in Intelligent Systems and Computing, Springer Verlag, 2015, pp. 771–782. doi: 10.1007/978-81-322-2250-7_77.
  • [18] X. E. Pantazi, D. Moshou, A. A. Tamouridou, and S. Kasderidis, “Leaf disease recognition in vine plants based on local binary patterns and one class support vector machines,” in IFIP Advances in Information and Communication Technology, Springer New York LLC, 2016, pp. 319–327. doi: 10.1007/978-3-319-44944-9_27.
  • [19] A. Johannes et al., “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case,” Comput Electron Agric, vol. 138, pp. 200–209, Jun. 2017, doi: 10.1016/j.compag.2017.04.013.
  • [20] J. Chen, H. Yin, and D. Zhang, “A self-adaptive classification method for plant disease detection using GMDH-Logistic model,” Sustainable Computing: Informatics and Systems, vol. 28, Dec. 2020, doi: 10.1016/j.suscom.2020.100415.
  • [21] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015. doi: 10.1038/nature14539.
  • [22] H. C. Altunay and Z. Albayrak, “A hybrid CNN + LSTMbased intrusion detection system for industrial IoT networks,” Engineering Science and Technology, an International Journal, vol. 38, Feb. 2023, doi: 10.1016/j.jestch.2022.101322.
  • [23] M. Çakmak and Z. Albayrak, “AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks,” Mobile Networks and Applications, vol. 27, no. 5, 2022, doi: 10.1007/s11036-022-02011-8.
  • [24] R. İncir and F. Bozkurt, “A study on effective data preprocessing and augmentation method in diabetic retinopathy classification using pre-trained deep learning approaches,” Multimed Tools Appl, vol. 83, no. 4, pp. 12185–12208, Jan. 2023, doi: 10.1007/S11042-023-15754-7/TABLES/7.
  • [25] D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” 2017, doi: 10.1146/annurev-bioeng-071516.
  • [26] S. NAHZAT, F. BOZKURT, and M. YAĞANOĞLU, “White Blood Cell Classification Using Convolutional Neural Network,” Journal of Scientific Technology and Engineering Research, 2022, doi: 10.53525/jster.1018213.
  • [27] F. Bozkurt, “A deep and handcrafted features-based framework for diagnosis of COVID-19 from chest x-ray images,” Concurr Comput, vol. 34, no. 5, 2022, doi: 10.1002/cpe.6725.
  • [28] A. Ahmad, D. Saraswat, and A. El Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agricultural Technology, vol. 3. 2023. doi: 10.1016/j.atech.2022.100083.
  • [29] J. Chen, A. Zeb, Y. A. Nanehkaran, and D. Zhang, “Stacking ensemble model of deep learning for plant disease recognition,” J Ambient Intell Humaniz Comput, vol. 14, no. 9, 2023, doi: 10.1007/s12652-022-04334-6.
  • [30] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput Electron Agric, vol. 173, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
  • [31] M. Nawaz, T. Nazir, A. Javed, S. Tawfik Amin, F. Jeribi, and A. Tahir, “CoffeeNet: A deep learning approach for coffee plant leaves diseases recognition,” Expert Syst Appl, vol. 237, 2024, doi: 10.1016/j.eswa.2023.121481.
  • [32] E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput Electron Agric, vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
  • [33] G. Geetharamani and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers and Electrical Engineering, vol. 76, pp. 323–338, Jun. 2019, doi: 10.1016/j.compeleceng.2019.04.011.
  • [34] B. Chang, Y. Wang, X. Zhao, G. Li, and P. Yuan, “A general-purpose edge-feature guidance module to enhance vision transformers for plant disease identification[Formula presented],” Expert Syst Appl, vol. 237, 2024, doi: 10.1016/j.eswa.2023.121638.
  • [35] A. L. Latifah, Lembaga Ilmu Pengetahuan Indonesia. Research Center for Informatics, Institute of Electrical and Electronics Engineers. Indonesia Section, and Institute of Electrical and Electronics Engineers, 2018 International Conference on Computer, Control, Informatics and its Applications : “Recent Challenges in Machine Learning for Computing Applications” : proceedings : November 1st-2nd, 2018, Tangerang, Indonesia.
  • [36] S. M. Hassan, M. Jasinski, Z. Leonowicz, E. Jasinska, and A. K. Maji, “Plant disease identification using shallow convolutional neural network,” Agronomy, vol. 11, no. 12, Dec. 2021, doi: 10.3390/agronomy11122388.
  • [37] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol Inform, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
  • [38] A. M. Fayyaz et al., “Leaf blights detection and classification in large scale applications,” Intelligent Automation and Soft Computing, vol. 31, no. 1, pp. 507–522, 2022, doi: 10.32604/IASC.2022.016392.
  • [39] A. Elaraby, W. Hamdy, and M. Alruwaili, “Optimization of deep learning model for plant disease detection using particle swarm optimizer,” Computers, Materials and Continua, vol. 71, no. 2, pp. 4019–4031, 2022, doi: 10.32604/cmc.2022.022161.
  • [40] A. N. Özalp and Z. Albayrak, “Detecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithms,” 2023.
  • [41] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2018, pp. 270–279. doi: 10.1007/978-3-030-01424-7_27.
  • [42] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition.” [Online]. Available: http://image-net.org/challenges/LSVRC/2015/
  • [43] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int J Comput Vis, vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/s11263-015-0816-y.
  • [44] K. Simonyan and A. Zisserman, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION,” 2015. [Online]. Available: http://www.robots.ox.ac.uk/
  • [45] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks.” [Online]. Available: https://github.com/liuzhuang13/DenseNet.
  • [46] A. Sardar, A. Issa, and Z. Albayrak, “DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM,” 2023.
  • [47] Q. Ji, J. Huang, W. He, and Y. Sun, “Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images,” Algorithms, vol. 12, no. 3, 2019, doi: 10.3390/a12030051.
  • [48] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.00567
  • [49] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.00567
  • [50] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.04381
  • [51] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available: http://arxiv.org/abs/1905.11946
There are 51 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Muhammet Çakmak 0000-0002-3752-6642

Early Pub Date April 27, 2024
Publication Date April 30, 2024
Submission Date January 12, 2024
Acceptance Date March 18, 2024
Published in Issue Year 2024

Cite

IEEE M. Çakmak, “Automatic Maize Leaf Disease Recognition Using Deep Learning”, SAUCIS, vol. 7, no. 1, pp. 61–76, 2024, doi: 10.35377/saucis...1418505.

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