Araştırma Makalesi
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Yıl 2024, Cilt: 7 Sayı: 1, 1 - 10, 30.04.2024

Emrah Dönmez , Yavuz Ünal , Hatice Kayhan

https://doi.org/10.35377/saucis...1359146

Öz

Kaynakça

  • [1] A. Jain, S. Sarsaiya, Q. Wu, Y. Lu, and J. Shi, ‘A review of plant leaf fungal diseases and its environment speciation’, Bioengineered, vol. 10, no. 1, pp. 409–424, Jan. 2019, doi: 10.1080/21655979.2019.1649520.
  • [2] E. Dönmez, ‘Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks’, Celal Bayar Üniversitesi Fen Bilimleri Dergisi, vol. 16, no. 3, pp. 323–331, Sep. 2020, doi: 10.18466/cbayarfbe.742889.
  • [3] E. Donmez, ‘Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features’, in 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey: IEEE, Oct. 2020, pp. 1–4. doi: 10.1109/SIU49456.2020.9302142.
  • [4] C. Jackulin and S. Murugavalli, ‘A comprehensive review on detection of plant disease using machine learning and deep learning approaches’, Measurement: Sensors, vol. 24, p. 100441, Dec. 2022, doi: 10.1016/j.measen.2022.100441.
  • [5] M. M. Taye, ‘Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions’, Computers, vol. 12, no. 5, p. 91, Apr. 2023, doi: 10.3390/computers12050091.
  • [6] A. Güneyli̇, C. E. Onursal, T. Seçmen, S. Sevi̇Nç Üzümcü, M. A. Koyuncu, and D. Erbaş, ‘The Use of Controlled Atmosphere Box in Sweet Cherry Storage’, Horticultural Studies, vol. 39, no. 2, pp. 33–40, Jun. 2022, doi: 10.16882/hortis.1119743.
  • [7] S. M. Hassan et al., ‘A Survey on Different Plant Diseases Detection Using Machine Learning Techniques’, Electronics, vol. 11, no. 17, p. 2641, Aug. 2022, doi: 10.3390/electronics11172641.
  • [8] K. Zhang, L. Zhang, and Q. Wu, ‘Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network’:, International Journal of Agricultural and Environmental Information Systems, vol. 10, no. 2, pp. 98–110, Apr. 2019, doi: 10.4018/IJAEIS.2019040105.
  • [9] M. Ilic, S. Ilic, S. Jovic, and S. Panic, ‘Early cherry fruit pathogen disease detection based on data mining prediction’, Computers and Electronics in Agriculture, vol. 150, pp. 418–425, Jul. 2018, doi: 10.1016/j.compag.2018.05.008.
  • [10] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, ‘Plant leaf disease classification using EfficientNet deep learning model’, Ecological Informatics, vol. 61, p. 101182, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
  • [11] R. C. Joshi, M. Kaushik, M. K. Dutta, A. Srivastava, and N. Choudhary, ‘VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant’, Ecological Informatics, vol. 61, p. 101197, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101197.
  • [12] R. G. De Luna, E. P. Dadios, and A. A. Bandala, ‘Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition’, in TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South): IEEE, Oct. 2018, pp. 1414–1419. doi: 10.1109/TENCON.2018.8650088.
  • [13] A. Özcan and E. Dönmez, ‘Bacterial Disease Detection for Pepper Plant by Utilizing Deep Features Acquired from DarkNet-19 CNN Model’, DÜMF Mühendislik Dergisi, pp. 573–579, Sep. 2021, doi: 10.24012/dumf.1001901.
  • [14] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, ‘Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks’, IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
  • [15] M. Islam, Anh Dinh, K. Wahid, and P. Bhowmik, ‘Detection of potato diseases using image segmentation and multiclass support vector machine’, in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON: IEEE, Apr. 2017, pp. 1–4. doi: 10.1109/CCECE.2017.7946594.
  • [16] K. P. Ferentinos, ‘Deep learning models for plant disease detection and diagnosis’, Computers and Electronics in Agriculture, vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/j.compag.2018.01.009.
  • [17] A. P. J, ‘Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network’. Mendeley, Apr. 18, 2019. doi: 10.17632/TYWBTSJRJV.1.
  • [18] J. Redmon and A. Farhadi, ‘YOLO9000: Better, Faster, Stronger’, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 6517–6525. doi: 10.1109/CVPR.2017.690.
  • [19] Seong, Song, Yoon, Kim, and Choi, ‘Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes Using a Convolutional Neural Network’, Sensors, vol. 19, no. 19, p. 4263, Sep. 2019, doi: 10.3390/s19194263.
  • [20] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, ‘KNN Model-Based Approach in Classification’, in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, vol. 2888, R. Meersman, Z. Tari, and D. C. Schmidt, Eds., in Lecture Notes in Computer Science, vol. 2888. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 986–996. doi: 10.1007/978-3-540-39964-3_62.
  • [21] W. Jia et al., ‘A classification algorithm with Linear Discriminant Analysis and Axiomatic Fuzzy Sets’, Mathematical Foundations of Computing, vol. 2, no. 1, pp. 73–81, 2019, doi: 10.3934/mfc.2019006.
  • [22] E. A. Zanaty, ‘Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification’, Egyptian Informatics Journal, vol. 13, no. 3, pp. 177–183, Nov. 2012, doi: 10.1016/j.eij.2012.08.002.

Bacterial Disease Detection of Cherry Plant Using Deep Features

Yıl 2024, Cilt: 7 Sayı: 1, 1 - 10, 30.04.2024

Emrah Dönmez , Yavuz Ünal , Hatice Kayhan

https://doi.org/10.35377/saucis...1359146

Öz

Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.

Anahtar Kelimeler

Cherry plant Classification Convolutional neural networks Machine learning Plant diseases

Teşekkür

We thank Amasya University and Bandırma Onyedi Eylül University for providing the opportunity to use computer laboratories in the realization of this study.

Kaynakça

  • [1] A. Jain, S. Sarsaiya, Q. Wu, Y. Lu, and J. Shi, ‘A review of plant leaf fungal diseases and its environment speciation’, Bioengineered, vol. 10, no. 1, pp. 409–424, Jan. 2019, doi: 10.1080/21655979.2019.1649520.
  • [2] E. Dönmez, ‘Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks’, Celal Bayar Üniversitesi Fen Bilimleri Dergisi, vol. 16, no. 3, pp. 323–331, Sep. 2020, doi: 10.18466/cbayarfbe.742889.
  • [3] E. Donmez, ‘Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features’, in 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey: IEEE, Oct. 2020, pp. 1–4. doi: 10.1109/SIU49456.2020.9302142.
  • [4] C. Jackulin and S. Murugavalli, ‘A comprehensive review on detection of plant disease using machine learning and deep learning approaches’, Measurement: Sensors, vol. 24, p. 100441, Dec. 2022, doi: 10.1016/j.measen.2022.100441.
  • [5] M. M. Taye, ‘Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions’, Computers, vol. 12, no. 5, p. 91, Apr. 2023, doi: 10.3390/computers12050091.
  • [6] A. Güneyli̇, C. E. Onursal, T. Seçmen, S. Sevi̇Nç Üzümcü, M. A. Koyuncu, and D. Erbaş, ‘The Use of Controlled Atmosphere Box in Sweet Cherry Storage’, Horticultural Studies, vol. 39, no. 2, pp. 33–40, Jun. 2022, doi: 10.16882/hortis.1119743.
  • [7] S. M. Hassan et al., ‘A Survey on Different Plant Diseases Detection Using Machine Learning Techniques’, Electronics, vol. 11, no. 17, p. 2641, Aug. 2022, doi: 10.3390/electronics11172641.
  • [8] K. Zhang, L. Zhang, and Q. Wu, ‘Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network’:, International Journal of Agricultural and Environmental Information Systems, vol. 10, no. 2, pp. 98–110, Apr. 2019, doi: 10.4018/IJAEIS.2019040105.
  • [9] M. Ilic, S. Ilic, S. Jovic, and S. Panic, ‘Early cherry fruit pathogen disease detection based on data mining prediction’, Computers and Electronics in Agriculture, vol. 150, pp. 418–425, Jul. 2018, doi: 10.1016/j.compag.2018.05.008.
  • [10] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, ‘Plant leaf disease classification using EfficientNet deep learning model’, Ecological Informatics, vol. 61, p. 101182, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
  • [11] R. C. Joshi, M. Kaushik, M. K. Dutta, A. Srivastava, and N. Choudhary, ‘VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant’, Ecological Informatics, vol. 61, p. 101197, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101197.
  • [12] R. G. De Luna, E. P. Dadios, and A. A. Bandala, ‘Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition’, in TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South): IEEE, Oct. 2018, pp. 1414–1419. doi: 10.1109/TENCON.2018.8650088.
  • [13] A. Özcan and E. Dönmez, ‘Bacterial Disease Detection for Pepper Plant by Utilizing Deep Features Acquired from DarkNet-19 CNN Model’, DÜMF Mühendislik Dergisi, pp. 573–579, Sep. 2021, doi: 10.24012/dumf.1001901.
  • [14] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, ‘Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks’, IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
  • [15] M. Islam, Anh Dinh, K. Wahid, and P. Bhowmik, ‘Detection of potato diseases using image segmentation and multiclass support vector machine’, in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON: IEEE, Apr. 2017, pp. 1–4. doi: 10.1109/CCECE.2017.7946594.
  • [16] K. P. Ferentinos, ‘Deep learning models for plant disease detection and diagnosis’, Computers and Electronics in Agriculture, vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/j.compag.2018.01.009.
  • [17] A. P. J, ‘Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network’. Mendeley, Apr. 18, 2019. doi: 10.17632/TYWBTSJRJV.1.
  • [18] J. Redmon and A. Farhadi, ‘YOLO9000: Better, Faster, Stronger’, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 6517–6525. doi: 10.1109/CVPR.2017.690.
  • [19] Seong, Song, Yoon, Kim, and Choi, ‘Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes Using a Convolutional Neural Network’, Sensors, vol. 19, no. 19, p. 4263, Sep. 2019, doi: 10.3390/s19194263.
  • [20] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, ‘KNN Model-Based Approach in Classification’, in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, vol. 2888, R. Meersman, Z. Tari, and D. C. Schmidt, Eds., in Lecture Notes in Computer Science, vol. 2888. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 986–996. doi: 10.1007/978-3-540-39964-3_62.
  • [21] W. Jia et al., ‘A classification algorithm with Linear Discriminant Analysis and Axiomatic Fuzzy Sets’, Mathematical Foundations of Computing, vol. 2, no. 1, pp. 73–81, 2019, doi: 10.3934/mfc.2019006.
  • [22] E. A. Zanaty, ‘Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification’, Egyptian Informatics Journal, vol. 13, no. 3, pp. 177–183, Nov. 2012, doi: 10.1016/j.eij.2012.08.002.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Emrah Dönmez 0000-0003-3345-8344

Yavuz Ünal 0000-0002-3007-679X

Hatice Kayhan 0000-0003-4679-7142

Erken Görünüm Tarihi 27 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 12 Eylül 2023
Kabul Tarihi 10 Ocak 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 1

Kaynak Göster

IEEE E. Dönmez, Y. Ünal, ve H. Kayhan, “Bacterial Disease Detection of Cherry Plant Using Deep Features”, SAUCIS, c. 7, sy. 1, ss. 1–10, 2024, doi: 10.35377/saucis...1359146.
 Kapak Resmi İndir

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License