Abstract
Nowadays, people need easy access to basic nutrients to live a healthy life. In addition to providing calories that can meet the physiological needs of human beings, maize, which is one of the basic foods, contains valuable minerals and vitamins such as vitamin B6, sodium, magnesium, zinc, potassium, calcium, vitamin A. As a result of the increase in the world population in the world and our country, the need for maize is increasing day by day. Herein, it is important to detect the diseases seen in maize leaves that reduce the efficiency of maize production. Thanks to the developing technologies, producers should be encouraged by using technological opportunities in maize cultivation. In the study, it is aimed to detect maize rust, gray leaf spot, and leaf blight on maize leaves. In addition, two models based on the EfficientNetB5 network and convolutional neural network have been developed to detect diseases found in maize leaves using deep learning methods. To increase the performance metrics of created models, the number of images has been increased by using data augmentation techniques (mirror, rotation, scale). From the results, it is seen that the prediction success rates obtained in the EfficientNetB5 transfer learning model and the developed deep learning model are equal to 92.12% and 89.88%, respectively.
Keywords
Deep learning image processing convolutional neural network (CNN) maize leaf diseases EfficientNetB
References
- Priyadharshini, A., R., Arivazhagan, S., Arun, M., & Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12), 8887–8895. https://doi.org/10.1007/s00521-019-04228-3.
- Alkan, A., Abdullah, MU., Abdullah, H.O., Assaf, M., Zhou, H., (2021). A smart agricultural application: automated detection of diseases in vine leaves using hybrid deep learning, Turkish Journal of Agriculture and Forestry. doi:10.3906/tar-2007-105.
- An, J., Li, W., Li, M., Cui, S., & Yue, H. (2019). Identification and classification of maize drought stress using deep convolutional neural network. Symmetry, 11(2), 1–14. https://doi.org/10.3390/sym11020256.
- Aurangzeb, K., Akmal, F., Khan, A., M., Sharif, M., & Javed, M. Y. (2020). Advanced Machine Learning Algorithm Based System for Crops Leaf Diseases Recognition. Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, 146–151. https://doi.org/10.1109/CDMA47397.2020.00031.
- Dataset, corn-or-maize-leaf-disease-dataset @ www.kaggle.com. (y.y.). Tarihinde adresinden erişildi https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset.
- Huang, Z., Qin, A., Lu, J., Menon, A., & Gao, J. (2020). Grape Leaf Disease Detection and Classification Using Machine Learning. Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020, (January), 870–877. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00150.
- Kusumo, B. S., Heryana, A., Mahendra, O., & Pardede, H. F. (2019). Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing. 2018 International Conference on Computer, Control, Informatics and its Applications: Recent Challenges in Machine Learning for Computing Applications, IC3INA 2018 - Proceeding, 93–97. https://doi.org/10.1109/IC3INA.2018.8629507.
- Lv, M., Zhou, G., He, M., Chen, A., Zhang, W., & Hu, Y. (2020). Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet. IEEE Access, 8, 57952–57966. https://doi.org/10.1109/ACCESS.2020.2982443.
- Sibiya, M., & Sumbwanyambe, M. (2019). A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering, 1(1), 119–131. https://doi.org/10.3390/agriengineering1010009.
- Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700.
- Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, 30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405.
- Zhang, Z., He, X., Sun, X., Guo, L., Wang, J., & Wang, F. (2015). Image recognition of maize leaf disease based on GA-SVM. Chemical Engineering Transactions, 46, 199–204. https://doi.org/10.3303/CET1546034.
- Zhao, Y.-X., Wang, K.-R., Bai, Z.-Y., Li, S.-K., Xie, R.-Z., & Gao, S.-J. (2009). Research of Maize Leaf Disease Identifying Models Based Image Recognition. Crop Modeling and Decision Support, 1(2004), 317–324. https://doi.org/10.1007/978-3-642-01132-0_35.
Abstract
Günümüzde insanların sağlıklı yaşayabilmeleri için temel besinlere kolayca erişebilmeleri gerekmektedir. Temel besinlerden olan mısırda insanoğlunun fizyolojik ihtiyaçlarını karşılayabilecek kalorinin sağlanması yanında mısırda yer alan B6 vitamini, sodyum, magnezyum, çinko, potasyum, kalsiyum, A vitamini gibi değerli mineraller ve vitaminler bulunmaktadır. Dünya’da ve ülkemizde dünya nüfusunun artmasıyla, mısıra olan ihtiyaç gün geçtikçe artmaktadır. Bu noktada, mısır üretiminin verimliliğini düşüren mısır yapraklarında görülen hastalıkların tespiti önemlidir. Gelişen teknolojiler sayesinde mısır yetiştiriciliğinde teknolojik imkânlar kullanılarak üreticilerin teşvik edilmesi gerekmektedir. Bu çalışma sayesinde, mısır yapraklarında görülen mısır pası, gri yaprak lekesi ve yaprak yanığı tespitinin gerçekleştirilmesi amaçlanmıştır. Ayrıca, derin öğrenme yöntemleri kullanılarak mısır yapraklarında görülen hastalıkların tespit edilebilmesi için EfficientNetB5 ağı ve evrişimsel sinir ağları tabanlı iki adet model geliştirilmiştir. Oluşturulan modellerin performans metriklerini arttırabilmek için, görüntülerin sayısı veri çoğaltma teknikleri kullanılarak (aynalama, döndürme, büyültme) arttırılmıştır. Sonuçlardan, EfficientNetB5 transfer öğrenmesi modeli ve geliştirilen derin öğrenme modelinde elde edilen tahmin başarı oranlarının sırasıyla %92.12 ve %89.88’e eşit olduğu görülmektedir.
Keywords
Derin öğrenme görüntü işleme evrişimli sinir ağları (ESA) mısır yaprağı hastalıkları EfficientNetB5
References
- Priyadharshini, A., R., Arivazhagan, S., Arun, M., & Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12), 8887–8895. https://doi.org/10.1007/s00521-019-04228-3.
- Alkan, A., Abdullah, MU., Abdullah, H.O., Assaf, M., Zhou, H., (2021). A smart agricultural application: automated detection of diseases in vine leaves using hybrid deep learning, Turkish Journal of Agriculture and Forestry. doi:10.3906/tar-2007-105.
- An, J., Li, W., Li, M., Cui, S., & Yue, H. (2019). Identification and classification of maize drought stress using deep convolutional neural network. Symmetry, 11(2), 1–14. https://doi.org/10.3390/sym11020256.
- Aurangzeb, K., Akmal, F., Khan, A., M., Sharif, M., & Javed, M. Y. (2020). Advanced Machine Learning Algorithm Based System for Crops Leaf Diseases Recognition. Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, 146–151. https://doi.org/10.1109/CDMA47397.2020.00031.
- Dataset, corn-or-maize-leaf-disease-dataset @ www.kaggle.com. (y.y.). Tarihinde adresinden erişildi https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset.
- Huang, Z., Qin, A., Lu, J., Menon, A., & Gao, J. (2020). Grape Leaf Disease Detection and Classification Using Machine Learning. Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020, (January), 870–877. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00150.
- Kusumo, B. S., Heryana, A., Mahendra, O., & Pardede, H. F. (2019). Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing. 2018 International Conference on Computer, Control, Informatics and its Applications: Recent Challenges in Machine Learning for Computing Applications, IC3INA 2018 - Proceeding, 93–97. https://doi.org/10.1109/IC3INA.2018.8629507.
- Lv, M., Zhou, G., He, M., Chen, A., Zhang, W., & Hu, Y. (2020). Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet. IEEE Access, 8, 57952–57966. https://doi.org/10.1109/ACCESS.2020.2982443.
- Sibiya, M., & Sumbwanyambe, M. (2019). A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering, 1(1), 119–131. https://doi.org/10.3390/agriengineering1010009.
- Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700.
- Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, 30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405.
- Zhang, Z., He, X., Sun, X., Guo, L., Wang, J., & Wang, F. (2015). Image recognition of maize leaf disease based on GA-SVM. Chemical Engineering Transactions, 46, 199–204. https://doi.org/10.3303/CET1546034.
- Zhao, Y.-X., Wang, K.-R., Bai, Z.-Y., Li, S.-K., Xie, R.-Z., & Gao, S.-J. (2009). Research of Maize Leaf Disease Identifying Models Based Image Recognition. Crop Modeling and Decision Support, 1(2004), 317–324. https://doi.org/10.1007/978-3-642-01132-0_35.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | PAPERS |
| Authors | |
| Publication Date | October 20, 2021 |
| Submission Date | August 31, 2021 |
| Acceptance Date | September 16, 2021 |
| Published in Issue | Year 2021 Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special |
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