Öz
Bitki
hastalıklarının hızlı ve doğru teşhisi için makine öğrenmesine dayalı
yaklaşımlar kullanılmaktadır. Son zamanlarda derin öğrenme yaklaşımı bitki
türleri ve hastalıkları tanıma ile ilgili problemlerde de kullanılmaktadır. Bu
çalışmada, kayısı hastalıklarının tespiti için Derin Evrişimsel Sinir Ağlarına
(DESA) dayalı bir model önerilmiştir. Bu model, Evrişim, Relu, Normalizasyon,
Havuzlama ve tam bağlı katmanlardan oluşmaktadır. Önerilen model için evrişim
katmanlarında kullanılan filtrelerin pencere boyutu 3×3, 5×5, 7×7, 9×9 ve 11×11
olmak üzere beş farklı filtre çeşitleri kullanılarak deneysel çalışmalar
gerçekleştirilmiştir. Önerilen çalışmayı test etmek için Bingöl ve İnönü
Üniversitelerinin Ziraat Fakültelerinin çalışma alanlarından elde edilen kayısı
hastalıklarından oluşan görüntüler kaydedilip kapsamlı bir veri tabanı inşa
edilmiştir. Geliştirilen derin ağ modeli bu veri tabanı üzerinde test
edilmiştir. Gerçekleştirilen deneysel sonuçlara göre, kayısı hastalıklarının
tespiti için önerilen derin ağ modeli diğer geleneksel görüntü
tanımlayıcılarına göre daha yüksek sınıflandırma başarısı elde edildiği gözlemlenmiştir.
Anahtar Kelimeler
Bitki Hastalık Tespiti Derin Öğrenme Kayısı Hastalık Görüntüleri Evrişimsel Sinir Ağları Geleneksel görüntü tanımlayıcıları.
Destekleyen Kurum
Bingöl Üniversitesi Bilimsel Araştırma Projeleri (BAP) Birimi
Proje Numarası
BAP-MMF.2018.00.004
Teşekkür
Bu çalışma Bingöl Üniversitesi Bilimsel Araştırma Projeleri programı tarafından desteklenmiştir (Proje Numarası: BAP-MMF.2018.00.004).
Kaynakça
- Turkoglu M., Hanbay D. 2015. Classification of the grape varieties based on leaf recognition by using SVM classifier. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2674-2677.
- Nguyen T.T.N., Van Tuan Le T.L.L., Vu H., Pantuwong N., Yagi Y. 2016. Flower species identification using deep convolutional neural networks. AUN/SEED-Net Regional Conference for Computer and Information Engineering.
- Turkoglu M., Hanbay D. 2018. Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1-4.
- Athanikar G., Badar P. 2016. Potato Leaf Diseases Detection and Classification System. International Journal of Computer Science and Mobile Computing, 5 (2): 76-88.
- Prashar K, 2017. Robust Automatic Cotton Crop Disease Recognition (ACDR) Method using the Hybrid Feature Descriptor with SVM. 4th 2016 International Conference on Computing on sustainable Global Development.
- Pydıpatı R., Burks T.F., Lee W.S. 2006. Identification of citrus disease using color te×ture features and discriminant analysis. Computers and electronics in agriculture, 52 (1-2): 49-59.
- Kulkarni A.H., Patil A. 2012. Applying image processing technique to detect plant diseases. International Journal of Modern Engineering Research, (2)5: 3661-3664.
- Singh K., Kumar S., Kaur P. 2017. Local Binary Patterns Based Detection of Rust Disease of Lentils (Lens Culinaris) Using K-NN Classification System. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 7 (4): 47-52.
- Dubey S.R., Jalal A.S. 2014. Fusing Color and Te×ture Cues to Categorize the Fruit Diseases from Images. ar×iv preprint ar×iv:1412.7277.
- Mokhtar U., El Bendary N., Hassenian A.E., Emary E., Mahmoud M.A., Hefny H., Tolba M.F. 2014. SVM-based detection of tomato leaves diseases. In Intelligent Systems, Springer, Cham, 641-652.
- Es-saady Y., El Massi I., El Yassa M., Mammass D., Benazoun A. (2016, May). Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. In 2016 International Conference on Electrical and Information Technologies (ICEIT) (pp. 561-566). IEEE.
- Wallelign S., Polceanu M., Buche C. 2018. Soybean Plant Disease Identification Using Convolutional Neural Network. In The Thirty-First International Flairs Conference.
- Fuentes A., Yoon S., Kim S., Park, D. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9): 2022.
- Sladojevic S., Arsenovic M., Anderla A., Culibrk D., Stefanovic D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience.
- Lu Y., Yi S., Zeng N., Liu Y., Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267: 378-384.
- Tan W.×., Zhao C.J., Wu H.R. 2016. CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors. High Technol. Lett., 22: 67–74.
- Amara J., Bouaziz B., Algergawy A. 2017. A Deep Learning-based Approach for Banana Leaf Diseases Classification. In: BTW Workshops; Bonn, Germany, 79-88.
- Ferentinos K. P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145: 311-318.
- Brahimi M., Boukhalfa K., Moussaoui A. 2017. Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4): 299-315.
- Mohanty S.P., Hughes D.P., Salath´e M. 2016. Using deep learning for image-based plant disease detection. Frontiers in plant science, 7: 1419.
- Kizrak M.A., Bolat B. 2018. Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma. Bilişim Teknolojileri Dergisi, 11(3): 263-286.
- Ülker E. 2017. Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3): 85-104.
- Doğan F., Türkoğlu İ. 2018. Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması. Sakarya University Journal of Computer and Information Sciences, 1(1): 10-21.
- Çarkacı N. 2018. Derin Öğrenme Uygulamalarında En Sık kullanılan Hiper-parametreler, https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (Erişim Tarihi: 12.10.2018).
Öz
Machine
learning approaches are used for fast and accurate diagnosis of plant diseases.
Recently, deep learning approach has been used in plant species and disease
recognition problems. In this study, a model based on Deep Convolutional Neural Networks (CNN)
was proposed for the detection of apricot diseases. The developed model consists
of Convolution, Relu, Normalization, Pooling, and fully connected layers. For the
proposed model, experimental studies were carried out using five different
filter types as 3×3, 5×5, 7×7, 9×9 and 11×11 window size of the filters used in
convolution layers. In order to test the proposed study, a comprehensive
database was constructed using the images of apricot diseases obtained from the
study areas of the Faculty of Agriculture of the Bingöl and İnönü Universities.
The developed deep network model has been tested on this database. According to
the experimental results carried out, it was observed that the proposed deep a network model for the detection of apricot diseases had a higher classification
success than other traditional image descriptors.
Anahtar Kelimeler
Plant Disease Detection Deep Learning Apricot Disease Images Convolutional Neural Networks Conventional Image Identifiers
Proje Numarası
BAP-MMF.2018.00.004
Kaynakça
- Turkoglu M., Hanbay D. 2015. Classification of the grape varieties based on leaf recognition by using SVM classifier. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2674-2677.
- Nguyen T.T.N., Van Tuan Le T.L.L., Vu H., Pantuwong N., Yagi Y. 2016. Flower species identification using deep convolutional neural networks. AUN/SEED-Net Regional Conference for Computer and Information Engineering.
- Turkoglu M., Hanbay D. 2018. Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1-4.
- Athanikar G., Badar P. 2016. Potato Leaf Diseases Detection and Classification System. International Journal of Computer Science and Mobile Computing, 5 (2): 76-88.
- Prashar K, 2017. Robust Automatic Cotton Crop Disease Recognition (ACDR) Method using the Hybrid Feature Descriptor with SVM. 4th 2016 International Conference on Computing on sustainable Global Development.
- Pydıpatı R., Burks T.F., Lee W.S. 2006. Identification of citrus disease using color te×ture features and discriminant analysis. Computers and electronics in agriculture, 52 (1-2): 49-59.
- Kulkarni A.H., Patil A. 2012. Applying image processing technique to detect plant diseases. International Journal of Modern Engineering Research, (2)5: 3661-3664.
- Singh K., Kumar S., Kaur P. 2017. Local Binary Patterns Based Detection of Rust Disease of Lentils (Lens Culinaris) Using K-NN Classification System. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 7 (4): 47-52.
- Dubey S.R., Jalal A.S. 2014. Fusing Color and Te×ture Cues to Categorize the Fruit Diseases from Images. ar×iv preprint ar×iv:1412.7277.
- Mokhtar U., El Bendary N., Hassenian A.E., Emary E., Mahmoud M.A., Hefny H., Tolba M.F. 2014. SVM-based detection of tomato leaves diseases. In Intelligent Systems, Springer, Cham, 641-652.
- Es-saady Y., El Massi I., El Yassa M., Mammass D., Benazoun A. (2016, May). Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. In 2016 International Conference on Electrical and Information Technologies (ICEIT) (pp. 561-566). IEEE.
- Wallelign S., Polceanu M., Buche C. 2018. Soybean Plant Disease Identification Using Convolutional Neural Network. In The Thirty-First International Flairs Conference.
- Fuentes A., Yoon S., Kim S., Park, D. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9): 2022.
- Sladojevic S., Arsenovic M., Anderla A., Culibrk D., Stefanovic D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience.
- Lu Y., Yi S., Zeng N., Liu Y., Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267: 378-384.
- Tan W.×., Zhao C.J., Wu H.R. 2016. CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors. High Technol. Lett., 22: 67–74.
- Amara J., Bouaziz B., Algergawy A. 2017. A Deep Learning-based Approach for Banana Leaf Diseases Classification. In: BTW Workshops; Bonn, Germany, 79-88.
- Ferentinos K. P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145: 311-318.
- Brahimi M., Boukhalfa K., Moussaoui A. 2017. Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4): 299-315.
- Mohanty S.P., Hughes D.P., Salath´e M. 2016. Using deep learning for image-based plant disease detection. Frontiers in plant science, 7: 1419.
- Kizrak M.A., Bolat B. 2018. Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma. Bilişim Teknolojileri Dergisi, 11(3): 263-286.
- Ülker E. 2017. Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3): 85-104.
- Doğan F., Türkoğlu İ. 2018. Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması. Sakarya University Journal of Computer and Information Sciences, 1(1): 10-21.
- Çarkacı N. 2018. Derin Öğrenme Uygulamalarında En Sık kullanılan Hiper-parametreler, https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (Erişim Tarihi: 12.10.2018).
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | BAP-MMF.2018.00.004 |
| Yayımlanma Tarihi | 13 Mart 2020 |
| Gönderilme Tarihi | 8 Mayıs 2019 |
| Kabul Tarihi | 16 Ekim 2019 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 9 Sayı: 1 |
