Derin Öğrenme ile Şeftali Hastalıkların Tespiti

Year 2021, Issue: 23, 540 - 546, 30.04.2021

Muzaffer Aslan

https://doi.org/10.31590/ejosat.883787

Cited By: 5

Abstract

Tarım ürünleri, dünyadaki canlıların beslenme ihtiyaçlarının karşılanması bakımından oldukça önemlidir. Dünya nüfusundaki hızlı artış tarımsal ürünlerde verimliğin arttırılmasını zorunlu hale getirmektedir. Sınırlı tarım alanlarında ürün verimliliğinin sağlanabilmesi bitkilerde görülebilecek hastalıklarının etkili bir şekilde ve zamanında tespiti oldukça önemlidir. Özellikle bazı meyve ağaçlarının kısa ömürlü olması bu ağaçlardaki hastalıkların doğru, zamanında ve hızlı bir şekilde tespitini daha önemli hale getirmektedir. Son zamanlarda görüntü işlemede yaygın olarak kullanılan derin öğrenme, tarımsal faaliyetlerde etkili uygulamalar sunmaktadır. Bu çalışmada, şeftali ağacı hastalıklarının tespiti için evrişimli sinir ağ yöntemi önerilmiştir. Önerilen yöntemde, daha önceden eğitilmiş AlexNet modeli ile şeftali ağaçlarında görülen monilya ve koşnili hastalık tespiti yapılmıştır. Deneysel çalışmalarda, TRB1 bölgesinden alınan gerçek hastalık görüntülerinden oluşan veri seti ile gerçekleştirildi. Yapılan deneysel çalışmalarda %99,30 doğruluk oranında hastalık tespiti yapılmıştır. Mevcut çalışmalardan %1,44 daha yüksek doğruluk oranına sağlandı.

Keywords

Derin öğrenme, Evrişimli sinir ağları, Bitki Hastalıkları

Detection of Peach Diseases with Deep Learning

Abstract

Agricultural products are very important in meeting the nutritional needs of living creatures in the world. The rapid increase in the world population makes it necessary to increase the productivity in agricultural products. It is very important to ensure product productivity in limited agricultural areas and to detect diseases that can be seen in plants effectively and on time. Especially the short life of some fruit trees makes it more important to detect the diseases in these trees accurately, on time and quickly. Deep learning, which has been widely used in image processing recently, offers effective applications in agricultural activities. In this study, convolutional neural network method is proposed to detect peach tree diseases. In the proposed method, the detection of the disease with monilya laxa and sphaerolecanium prunastri in peach trees was made with the previously trained AlexNet model. Experimental studies were carried out with a dataset consisting of real disease images taken from the TRB1 region. In experimental studies, the disease was detected with an accuracy of 99.30%. Achieved 1.44% higher accuracy than existing studies. 

Keywords

Deep Learing, Convolution Neural Network, Plant diseases

References

• Ayyüce Kızrak. 2018. “Derine Daha Derine: Evrişimli Sinir Ağları.” Retrieved (https://medium.com/@ayyucekizrak/deri̇ne-daha-deri̇ne-evrişimli-sinir-ağları-2813a2c8b2a9). Erişim Tarihi: 4 Şubat 2021
• Budak, Umit, Ömer Faruk Alçin, Muzaffer Aslan, and Abdulkadir Şengür. 2018. “Optic Disc Detection in Retinal Images via Faster Regional Convolutional Neural Networks.” in In 1st International Engineering and Technology Symposium (IETS-2018).
• Chen, Chunhua, and Yun Q. Shi. 2008. “JPEG Image Steganalysis Utilizing Both Intrablock and Interblock Correlations.” Pp. 3029–32 in Proceedings - IEEE International Symposium on Circuits and Systems.
• Chen, Junde, Huayi Yin, and Defu Zhang. 2020. “A Self-Adaptive Classification Method for Plant Disease Detection Using GMDH-Logistic Model.” Sustainable Computing: Informatics and Systems 28. doi: 10.1016/j.suscom.2020.100415.
• Demir, Fatih, Muammer Turkoglu, Muzaffer Aslan, and Abdulkadir Sengur. 2020. “A New Pyramidal Concatenated CNN Approach for Environmental Sound Classification.” Applied Acoustics 170. doi: 10.1016/j.apacoust.2020.107520.
• Fırıldak, Kasım, and Muhammed Fatih Talu. 2019. “Evrişimsel Sinir Ağlarında Kullanılan Transfer Öğrenme Yaklaşımlarının İncelenmesi.” Anatolian Journal of Computer Science 4(2):88–95.
• Gunavathi, C., K. Sivasubramanian, P. Keerthika, and C. Paramasivam. 2020. “A Review on Convolutional Neural Network Based Deep Learning Methods in Gene Expression Data for Disease Diagnosis.” Materials Today: Proceedings.
• Al Hiary, H., S. Bani Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh. 2011. “Fast and Accurate Detection and Classification of Plant Diseases.” International Journal of Computer Applications 17(1):31–38. doi: 10.5120/2183-2754.
• Hu, Gensheng, Xiaowei Yang, Yan Zhang, and Mingzhu Wan. 2019. “Identification of Tea Leaf Diseases by Using an Improved Deep Convolutional Neural Network.” Sustainable Computing: Informatics and Systems 24. doi: 10.1016/j.suscom.2019.100353.
• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2017. “ImageNet Classification with Deep Convolutional Neural Networks.” Communications of the ACM 60(6):84–90. doi: 10.1145/3065386.
• Liu, Bin, Zefeng Ding, Liangliang Tian, Dongjian He, Shuqin Li, and Hongyan Wang. 2020. “Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks.” Frontiers in Plant Science 11. doi: 10.3389/fpls.2020.01082.
• Liu, Bin, Yun Zhang, Dong Jian He, and Yuxiang Li. 2018. “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks.” Symmetry. doi: 10.3390/sym10010011.
• Mangathayaru, Nimmala, B. Mathura Bai, and Panigrahi Srikanth. 2018. “Clustering and Classification of Effective Diabetes Diagnosis: Computational Intelligence Techniques Using PCA with KNN.” Pp. 426–40 in Smart Innovation, Systems and Technologies. Vol. 83.
• Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. 2016. “Using Deep Learning for Image-Based Plant Disease Detection.” Frontiers in Plant Science 7(September). doi: 10.3389/fpls.2016.01419.
• Pevný, Tomáš, Patrick Bas, and Jessica Fridrich. 2010. “Steganalysis by Subtractive Pixel Adjacency Matrix.” IEEE Transactions on Information Forensics and Security 5(2):215–24. doi: 10.1109/TIFS.2010.2045842.
• Pires, Rillian Diello Lucas, Diogo Nunes Gonçalves, Jonatan Patrick Margarido Oruê, Wesley Eiji Sanches Kanashiro, Jose F. Rodrigues, Bruno Brandoli Machado, and Wesley Nunes Gonçalves. 2016. “Local Descriptors for Soybean Disease Recognition.” Computers and Electronics in Agriculture 125:48–55. doi: 10.1016/j.compag.2016.04.032.
• Prabhu, Raghav. 2018. “Understanding of Convolutional Neural Network (CNN) Deep Learning.” Medium.Com 1–11. Retrieved (https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148). Erişim Tarihi: 4 Şubat 2021
• Sahu, Aditya Kumar, and Gandharba Swain. 2019. “Dual Stego-Imaging Based Reversible Data Hiding Using Improved LSB Matching.” International Journal of Intelligent Engineering and Systems 12(5):63–73. doi: 10.22266/ijies2019.1031.07.
• Savary, Serge. 2020. “Plant Health and Food Security.” Journal of Plant Pathology 102(3):605–7. doi: 10.1007/s42161-020-00611-5.
• Siuly, Siuly, Omer Faruk Alcin, Enamul Kabir, Abdulkadir Sengur, Hua Wang, Yanchun Zhang, and Frank Whittaker. 2020. “A New Framework for Automatic Detection of Patients with Mild Cognitive Impairment Using Resting-State EEG Signals.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(9):1966–76. doi: 10.1109/TNSRE.2020.3013429.
• Tumen, Vedat, Ozal Yildirim, and Burhan Ergen. 2018. “Detection of Driver Drowsiness in Driving Environment Using Deep Learning Methods.” 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting, EBBT 2018 1–5. doi: 10.1109/EBBT.2018.8391427.
• Türkoğlu, Muammer, and Davut Hanbay. 2019a. “Plant Disease and Pest Detection Using Deep Learning-Based Features.” Turkish Journal of Electrical Engineering and Computer Sciences 27(3):1636–51. doi: 10.3906/elk-1809-181.
• Türkoğlu, Muammer, and Davut Hanbay. 2019b. “Plant Recognition System Based on Extreme Learning Machine by Using Shearlet Transform and New Geometric Features.” Journal of the Faculty of Engineering and Architecture of Gazi University 34(4):2097–2112. doi: 10.17341/gazimmfd.423674.
• Turkoglu, Muammer, Davut Hanbay, and Abdulkadir Sengur. 2019. “Multi-Model LSTM-Based Convolutional Neural Networks for Detection of Apple Diseases and Pests.” Journal of Ambient Intelligence and Humanized Computing (0123456789). doi: 10.1007/s12652-019-01591-w.
• Xie, Xiaoyue, Yuan Ma, Bin Liu, Jinrong He, Shuqin Li, and Hongyan Wang. 2020. “A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks.” Frontiers in Plant Science 11. doi: 10.3389/fpls.2020.00751.
• Yan, Qian, Baohua Yang, Wenyan Wang, Bing Wang, Peng Chen, and Jun Zhang. 2020. “Apple Leaf Diseases Recognition Based on an Improved Convolutional Neural Network.” Sensors (Switzerland) 20(12):1–14. doi: 10.3390/s20123535.
• Yann LeCun Geoffrey Hinton, Yoshua Bengio. 2015. “Deep Learning (2015), Y. LeCun, Y. Bengio and G. Hinton.” Nature.
• Zhang, Xihai, Yue Qiao, Fanfeng Meng, Chengguo Fan, and Mingming Zhang. 2018. “Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks.” IEEE Access 6:30370–77. doi: 10.1109/ACCESS.2018.2844405.