Bacterial Disease Detection for Pepper Plant by Utilizing Deep Features Acquiredfrom DarkNet-19 CNN Model

In recent years, computer-aided agriculture applications have been developing rapidly as a prominentresearch area. In parallel with the developments in technology, the use of automatic systems, sensor fusion,the internet of things, and artificial intelligence-based systems is becoming widespread in agriculture. Theuse of these systems allows for safer, faster, and more cost-effective operations based on human factors inagricultural applications. Among these applications, there are artificial intelligence applications developedbased on image processing and machine learning. Plant disease detection systems are also among theseartificial intelligence studies. Within the scope of this study: I. It has been ensured that the leaf images ofthe pepper plant have been segmented and their features have been extracted from the pre-trainedconvolutional neural network. II. These obtained features have been classified through the classifiermethods in order to detect bacterial disease. In the study, a total of 2475 images of pepper leaves with1478 healthy and 997 bacterial diseases, which are among the PlantVillage data sets, have been used. Toextract the features, the DarkNet-19 network model has been used as a pre-trained convolutional network.The SoftMax classifier in the last layer of the convolutional network model has been removed from thenetwork and SVM, KNN, and Decision-Tree-based classifiers are used instead of it. According to theresults, the level of performance achieved using the DarkNet-19 network and SVM classifier is quitesatisfactory.

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[1] S.S. Abu-Naser, K.A. Kashkash and M. Fayyad, “Developing an Expert System for Plant Disease Diagnosis,” Journal of Artificial Intelligence, vol. 1, no. 2, pp. 78-85, 2008.
[2] K. Gowthami, M. Pratyusha and B. Somasekhar, “Detection of diseases in different plants using digital image processing,” International Journal of Scientific Research in Eng., vol. 2, no. 2, pp. 18-23, 2017.
[3] M.A. Ebrahimi, M.H. Khoshtaghaza, S. Minaei and B. Jamshidi, “Vision-based pest detection based on SVM classification method,” Computers and Electronics in Agriculture, 137, pp. 52-58, 2017.
[4] N. Guettari, A.S. Capelle-Laizé and P. CarréBlind, “Image steg analysis based on evidential k-nearest neighbors,” IEEE International Conference on Image Processing (ICIP), pp. 2742-2746, 2016.
[5] F. Jobin, D. Anto and K. Anoop, “Identification of leaf diseases in pepper plants using soft computing techniques,” pp. 168-173, 2016.
[6] S. H. Lee, C. S. Chan, S. J. Mayo and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognition, 71, pp. 1-13, 2017.
[7] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, 145, pp. 311-318, 2018.
[8] M. Islam, A. Dinh, K. Wahid and P. Bhowmik, “Detection of potato diseases using image segmentation and multiclass support vector machine,” IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, 2017.
[9] Y.C. Zhang, H.P. Mao, B. Hu and M.X. Li, “Features selection of cotton disease leaves image based on fuzzy feature selection techniques,” IEEE proceedings of the 2007 international conference on wavelet analysis and pattern recognition, 2007.
[10]S.R. Dubey and A.S. Jalal, “Detection and classification of apple fruit diseases using complete local binary patterns,” IEEE 2012 third international conference on computer and communication technology, 2012.
[11]G. Owomugisha and E. Mwebaze, “Machine learning for plant disease incidence and severity measurements from leaf images,” 15th IEEE international conference on machine learning and applications, 2015.
[12]A. Meunkaewjinda, P. Kumsawat, K. Attakitmongcol, and A. Srikaew, “Grape leaf disease detection from color imagery using hybrid intelligent system,” IEEE 5th Int. Conf. on Electrical Engineering/Electronics. Computer, Telecom. and Inf. Technology, 2008.
[13]M. R. Naik and C.M.R. Sivappagari, “Plant leaf and disease detection by using HSV features and SVM classifier,” International journal of engineering science and computing, vol. 6, no. 12, pp. 3794-3797, 2016.
[14]B. Liu, Y. Zhang, D. He and Y. Li, “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks,” Symmetry, vol. 10, no. 1, pp. 11, 2017.
[15]C. DeChant, T. Wiesner-Hanks, S. Chen, E. L. Stewart, J. Yosinski, M. A. Gore and R.J. Nelson, H. Lipson, “Automated Identification of Northern LeafBlight-Infected Maize Plantsfrom Field Imagery Using Deep Learning,” Phytopathology, vol. 107, no. 11, pp. 1426–1432, 2017.
[16]J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong and Z. Sun, “A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network,” Computers and Electronics in Agriculture, vol. 154, no. 09, pp. 18–24, 2018.
[17]Y. Lu, S. Yi, N. Zeng, Y. Liu and Y. Zhang, “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, 267, pp. 378–384, 2017.
[18]S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, 2016.
[19]M.H. Saleem, J. Potgieter and K. M. Arif “Plant Disease Detection and Classification by Deep Learning,”. Plants, vol. 8, no. 11, pp. 468, 2019.
[20]E. Dönmez and P. V. Zadeh “A modified graph based approach for leaf segmentation with GPGPU support,” 23nd Signal Processing and Communications Applications Conference (SIU), pp. 1797-1800, 2015.
[21]E. Dönmez and A. F. Kocamaz, “A Hog & Graph Based Human Segmentation from Video Sequences,” International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1-5, 2018.
[22]E. Dönmez, “Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features,” 28th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2020.
[23]E. Dönmez, “Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks,” Celal Bayar University Journal of Science, vol. 16, no. 3, pp. 323-331, 2020.