DISEASE DETECTION FROM CASSAVA LEAF IMAGES WITH DEEP LEARNING METHODS IN WEB ENVIRONMENT

Yıl 2021, Cilt: 5 Sayı: 3, 625 - 644, 30.12.2021

Sedat Metlek

https://doi.org/10.46519/ij3dptdi.1029357

Cited By: 6

Öz

In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the “Cassava-Leaf-Disease-Classification” data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms. The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. The methods obtained as a result of the compilation are integrated into the ASP.NET MVC5 programming language. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.

DISEASE DETECTION FROM CASSAVA LEAF IMAGES WITH DEEP LEARNING METHODS IN WEB ENVIRONMENT

Öz

In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the “Cassava-Leaf-Disease-Classification” data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms.
The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. The methods obtained as a result of the compilation are integrated into the ASP.NET MVC5 programming language. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.

Anahtar Kelimeler

Deep learning, convolution neural network, image processing, cassava leaf diseases.

Kaynakça

• 1. Bahar, N. H. A., Lo, M., Sanjaya, M., Van Vianen, J., Alexander, P., Ickowitz, A., Sunderland, T., "Meeting the food security challenge for nine billion people in 2050: What impact on forests?", Global Environmental Change, Vol. 62, Pages 102056, 2020.
• 2. Mohanty, S. P., Hughes, D. P., Salathé, M., "Using Deep Learning for Image-Based Plant Disease Detection", Frontiers in Plant Science, Vol. 7, Pages 1419, 2016.
• 3. Harvey, C. A., Rakotobe, Z. L., Rao, N. S., Dave, R., Razafimahatratra, H., Rabarijohn, R. H., Rajaofara, H., MacKinnon, J. L., "Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar", Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 369, Pages 20130089, 2014.
• 4. Savary, S., Ficke, A., Aubertot, J.-N., Hollier, C., "Crop losses due to diseases and their implications for global food production losses and food security", Food Security, Vol. 4, Pages 519–537, 2012.
• 5. Jiang, H., Li, X., Safara, F., "IoT-based Agriculture: Deep Learning in Detecting Apple Fruit Diseases", Microprocessors and Microsystems, , Pages 104321, 2021.
• 6. Han, Z., Xu, A., "Ecological evolution path of smart education platform based on deep learning and image detection", Microprocessors and Microsystems, Vol. 80, Pages 103343, 2021.
• 7. Pallagani, V., Khandelwal, V., Chandra, B., Udutalapally, V., Das, D., Mohanty, S. P., "dCrop: A Deep-Learning Based Framework for Accurate Prediction of Diseases of Crops in Smart Agriculture", In 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), Pages 29–33, Rourkela, 2019
• 8. Zhou, C., Hu, J., Xu, Z., Yue, J., Ye, H., Yang, G., "A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique", Frontiers in plant science, Vol. 11, Pages 559, 2020. 9. Chambers, R., Ghildyal, B. P., "Agricultural research for resource-poor farmers: The farmer-first-and-last model", Agricultural Administration, Vol. 20, Pages 1–30, 1985.
• 10. Kontoes, C., Wilkinson, G. G., Burrill, A., Goffredo, S., Mégier, J., "An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture", International Journal of Geographical Information Systems, Vol. 7, Pages 247–262, 1993. http://doi:10.1080/02693799308901955.
• 11. Harris, R., "Remote sensing of agriculture change in Oman", International Journal of Remote Sensing, Vol. 24, Pages 4835–4852, 2003.
• 12. Sahoo, R. N., Ray, S. S., Manjunath, K. R., "Hyperspectral remote sensing of agriculture", Current Science, Vol. 108, Pages 848–859, 2015.
• 13. Makerere University, A. L., "Cassava Leaf Disease Classification", https://www.kaggle.com/c/cassava-leaf-disease-classification/overview/description, September 2, 2021.
• 14. Li, J., Tao, H., Shuhong, L., Salih, S. Q., Zain, J. M., Yankun, L., Vivekananda, G. N., Thanjaivadel, M., "Internet of things assisted condition-based support for smart manufacturing industry using learning technique", Computational Intelligence, Vol. 36, Pages 1737–1754, 2020.
• 15. López, M. M., Bertolini, E., Olmos, A., Caruso, P., Gorris, M. T., Llop, P., Penyalver, R., Cambra, M., "Innovative tools for detection of plant pathogenic viruses and bacteria", International Microbiology, Vol. 6, Pages 233–243, 2003.
• 16. Udendhran, R., Balamurugan, M., Suresh, A., Varatharajan, R., "Enhancing image processing architecture using deep learning for embedded vision systems", Microprocessors and Microsystems, Vol. 76, Pages 103094, 2020.
• 17. Gonzalez-Huitron, V., León-Borges, J. A., Rodriguez-Mata, A. E., Amabilis-Sosa, L. E., Ramírez-Pereda, B., Rodriguez, H., "Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4", Computers and Electronics in Agriculture, Vol. 181, Pages 105951, 2021.
• 18. Ngo, T. N., Rustia, D. J. A., Yang, E.-C., Lin, T.-T., "Automated monitoring and analyses of honey bee pollen foraging behavior using a deep learning-based imaging system", Computers and Electronics in Agriculture, Vol. 187, Pages 106239, 2021.
• 19. Kayaalp, K., Metlek, S., "Classification of Robust and Rotten Apples by Deep Learning Algorithm", Sakarya University Journal of Computer and Information Sciences, Vol. 3, Pages 111–119, 2020.
• 20. LeCun, Y., Bengio, Y., Hinton, G., "Deep learning", Nature, Vol. 521, Pages 436–444, 2015.
• 21. Cetiner, I., Var, A. A., Cetiner, H., "Classification of Knot Defect Types Using Wavelets and KNN", Elektronika ir Elektrotechnika, Vol. 22, Pages 67–72, 2016.
• 22. Metlek, S., Kayaalp, K., "Detection of bee diseases with a hybrid deep learning method", Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 36, Pages 1715–1732, 2021.
• 23. Rastogi, S., Singh, J., "A systematic review on machine learning for fall detection system", Computational Intelligence, Vol. 37, Pages 951–974, 2021.
• 24. Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., Joly, A., "Going deeper in the automated identification of Herbarium specimens", BMC Evolutionary Biology, Vol. 17, Pages 181, 2017.
• 25. Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y., "Identification of rice diseases using deep convolutional neural networks", Neurocomputing, Vol. 267, Pages 378–384, 2017.
• 26. Zhang, Y.-D., Dong, Z., Chen, X., Jia, W., Du, S., Muhammad, K., Wang, S.-H., "Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation", Multimedia Tools and Applications, Vol. 78, Pages 3613–3632, 2019.
• 27. Steinbrener, J., Posch, K., Leitner, R., "Hyperspectral fruit and vegetable classification using convolutional neural networks", Computers and Electronics in Agriculture, Vol. 162, Pages 364–372, 2019.
• 28. Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., Valenzuela, A., "A Review of Convolutional Neural Network Applied to Fruit Image Processing", Applied Science, Vol.10, Pages 1-31, 2020.
• 29. Zheng, Y.-Y., Kong, J.-L., Jin, X.-B., Wang, X.-Y., Zuo, M., "CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture", Sensors, Vol. 19, Pages 1058, 2019.
• 30. Wegner, J. D., Branson, S., Hall, D., Schindler, K., Perona, P., "Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6014–6023, Las Vegas, 2016.
• 31. Fanou, A., Valerien, Z., Wydra, K., "Cassava Bacterial Blight: A Devastating Disease of Cassava", Waisundara, V, Cassava, Pages 13-24, Erfurt, 2018
• 32. Tomlinson, K. R., Bailey, A. M., Alicai, T., Seal, S., Foster, G. D., "Cassava brown streak disease: historical timeline, current knowledge and future prospects", Molecular Plant Pathology, Vol. 19, Pages 1282–1294, 2018.
• 33. McCallum, E. J., Anjanappa, R. B., Gruissem, W., "Tackling agriculturally relevant diseases in the staple crop cassava (Manihot esculenta)", Current Opinion in Plant Biology, Vol. 38, Pages 50–58, 2017.
• 34. Legg, J. P., Lava Kumar, P., Makeshkumar, T., Tripathi, L., Ferguson, M., Kanju, E., Ntawuruhunga, P., Cuellar, W., "Cassava Virus Diseases", In Advances in virus research, Vol. 91, Pages 85–142, 2015.
• 35. Patil, B. L., Fauquet, C. M., "Cassava mosaic geminiviruses: actual knowledge and perspectives", Molecular plant pathology, Vol. 10, Pages 685–701, 2009.
• 36. Owor, B., Legg, J. P., Okao-Okuja, G., Obonyo, R., Ogenga-Latigo, M. W., "The effect of cassava mosaic geminiviruses on symptom severity, growth and root yield of a cassava mosaic virus disease-susceptible cultivar in Uganda", Annals of Applied Biology, Vol. 145, Pages 331–337, 2004.
• 37. Rajan, J. P., Rajan, S. E., Martis, R. J., Panigrahi, B. K., "Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System", Journal of Medical Systems, Vol. 44, Pages 34, 2019.
• 38. Dawud, A. M., Yurtkan, K., Oztoprak, H., "Corrigendum to “Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning”", Computational Intelligence and Neuroscience, Vol. 2020, Pages 1–1, 2020.
• 39. Metlek, S., Kılınç, E. E., Determination of Heart Disease By Machine Learning Methods. In 5th International Gap Mathematics-Engineering-Science and Health Sciences Congress; pp. 48–74, Urfa, 2020.
• 40. Mengi, D. F., Metlek, S., "Modeling Belongs To Turkey’s Mediterranean Coast Wind Of Exergy Multılayer Neural Network", International Journal of Engineering and Innovative Research, Vol. 2, Pages 102–120, 2020.
• 41. Metlek, S., Kayaalp, K., Basyigit, I. B., Genc, A., Dogan, H., "The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model", International Journal of RF and Microwave Computer-Aided Engineering, Vol. 31, Pages e22496, 2021.
• 42. Basyigit, I. B., Doğan, H., "The analytical and artificial intelligence methods to investigate the effects of aperture dimension ratio on electrical shielding effectiveness", International Journal of Electronics and Telecommunications, Vol. 65, Pages 359–365, 2019.
• 43. Basyigit, I. B., Genc, A., Dogan, H., Senel, F. A., Helhel, S., "Deep learning for both broadband prediction of the radiated emission from heatsinks and heatsink optimization", Engineering Science and Technology, an International Journal, Vol. 24, Pages 706–714, 2021.
• 44. Rajpal, S., Lakhyani, N., Singh, A. K., Kohli, R., Kumar, N., "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images", Chaos, Solitons & Fractals, Vol. 145, Pages 110749, 2021.
• 45. Narin, A., Kaya, C., Pamuk, Z., "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks", Pattern Analysis and Applications, Vol. 24, Pages 1207–1220, 2021.
• 46. Liu, T., Chen, M., Zhou, M., Du, S. S., Zhou, E., Zhao, T., "Towards understanding the importance of shortcut connections in residual networks", Computer Science, Vol. 1, Pages 1–27, 2019.
• 47. Kensert, A., Harrison, P. J., Spjuth, O., "Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes", Slas Dıscovery: Advancing the Science of Drug Discovery, Vol. 24, Pages 466–475, 2019.
• 48. He, K., Zhang, X., Ren, S., Sun, J., "Deep Residual Learning for Image Recognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Las Vegas, 2016.
• 49. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., "MobileNetV2: Inverted Residuals and Linear Bottlenecks", IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, Salt Lake City, 2018.
• 50. Souid, A., Sakli, N., Sakli, H., "Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2", Applied Sciences, Vol. 11, Pages 2751, 2021.
• 51. Tsai, C., Lai, Y., Perng, J., Tsui, I., Chung, Y., "Design and Application of an Autonomous Surface Vehicle with an AI-Based Sensing Capability" IEEE Underwater Technology (UT), pp. 1–4, Kaohsiung, 2019.
• 52. Toğaçar, M., Cömert, Z., Ergen, B., "Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks", Chaos, Solitons & Fractals, Vol. 144, Pages 110714, 2021.
• 53. Chicco, D., Tötsch, N., Jurman, G., "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation", BioData Mining, Vol. 14, Pages 13, 2021.
• 54. Kuşcu, Ö., Çetiner, H., Çetin, Ö., "Development of a web interface for performing morphological operations on CUDA platform", Computer Applications in Engineering Education, Vol. 24, Pages 787–798, 2016.
• 55. Mwebaze, E., Gebru, T., Frome, A., Nsumba, S., Tusubira, J., "iCassava 2019 fine-grained visual categorization challenge", Computer Science, Vol. 1, Pages 28–47, 2019.
• 56. Rao, P. K., "Cassava Leaf Disease Classification using Separable Convolutions UNet", Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol. 12, Pages 140–145, 2021.