Citrus Diseases Recognition by Using CNN
Pattern recognition is attracting the interest of researchers in the recently few years as a machine learning approaches due to its vast extending application areas. he application area includes communications, medicine, automations, data mining, military intelligence, document classification, bioinformatics, speech recognition and business.
In this research convolutional neural networks (CNN) using for building system to recognize diseases that are happened in citrus. In this study presented dataset for seven classes of citrus diseases which contains 2450 images such as anthracnose, brown rot, citrus black spot, citrus canker, citrus scob, melanose and sooty mold citrus. The proposed system recognizes learned via CNN. The experimental result shows our model has ability to recognize citrus diseases with high and robustness accuracy. The study presented here gives 88% recognition of citrus diseases for the entire database.
Li, Kangshun, Miaopeng Chen, Juchuang Lin, and Shanni Li. "Citrus Disease and Pest Recognition Algorithm Based on Migration Learning." In International Symposium on Intelligence Computation and Applications, pp. 3-20. Springer, Singapore, 2019.
Ferentinos, Konstantinos P. "Deep learning models for plant disease detection and diagnosis." Computers and Electronics in Agriculture 145 (2018): 311-318.
Tian, L. G., C. Liu, Y. Liu, M. Li, J. Y. Zhang, and H. L. Duan. "Research on plant diseases and insect pests identification based on CNN." In IOP Conference Series: Earth and Environmental Science, vol. 594, no. 1, p. 012009. IOP Publishing, 2020.
Sergey G.," An Introduction to Convolutional Neural Networks and Deep Learning with Caffe ", contentlab.io,APR 17, 2019.
Pan, W., Qin, J., Xiang, X., Wu, Y., Tan, Y., & Xiang, L. "A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks." IEEE Access 7 (2019): 87534-87542.
Manavalan, R. "Automatic identification of diseases in grains crops through computational approaches: A review." Computers and Electronics in Agriculture 178 (2020): 105802.
Liu, Z., Xiang, X., Qin, J., Ma, Y., Zhang, Q. and Xiong, N.N., "Image Recognition of Citrus Diseases Based on Deep Learning." CMC-COMPUTERS MATERIALS & CONTINUA 66, no. 1 (2021): 457-466.
Rauf, H.T., Saleem, B.A., Lali, M.I.U., Khan, M.A., Sharif, M. and Bukhari., "A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning." Data in brief 26 (2019): 104340.
Sharif, M., Khan, M.A., Iqbal, Z., Azam, M.F., Lali, M.I.U. and Javed, M.Y., "Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection." Computers and electronics in agriculture 150 (2018): 220-234.
Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M., & Saleem, B. A.. Saleem. "Symptom based automated detection of citrus diseases using color histogram and textural descriptors." Computers and Electronics in agriculture 138 (2017): 92-104.
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M. and Farhan, L., "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions." Journal of big Data 8, no. 1 (2021): 1-74.
Krizhevsky, A., Sutskever, I., & Hinton, G. E., "ImageNet classification with deep convolutional neural networks." Communications of the ACM 60, no. 6 (2017): 84-90.
Hossain, Md Anwar, and Md Shahriar Alam Sajib. "Classification of image using convolutional neural network (CNN)." Global Journal of Computer Science and Technology (2019).
A. Qayyum, C. K. Ang, S. Sridevi, M. K. A. A. Khan, L. W. Hong, M. Mazher, T. D. Chung, “Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images,” 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1-5, 2020.
Phan, T.H., Tran, D.C. and Hassan, M.F.,"Vietnamese character recognition based on CNN model with reduced character classes." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 962-969.
Jianxin Wu,"Introduction to Convolutional Neural Networks",National Key Lab for Novel Software Technology, Nanjing University, 2017.
Sultana, F., Sufian, A., & Dutta, P., "Advancements in image classification using convolutional neural network." 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2018.
Zhou, Yu, Haipeng Wang, Feng Xu, and Ya-Qiu Jin. "Polarimetric SAR image classification using deep convolutional neural networks." IEEE Geoscience and Remote Sensing Letters 13, no. 12 (2016): 1935-1939.
Mou, L. and Jin, Z., "Tree-Based Convolutional Neural Networks": Principles and Applications. Springer, 2018.
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S., "A survey of the recent architectures of deep convolutional neural networks." Artificial Intelligence Review 53, no. 8 (2020): 5455-5516.
Zhao, S. Y., Xie, Y. P., & Li, W. J., "Stochastic Normalized Gradient Descent with Momentum for Large Batch Training." arXiv preprint arXiv:2007.13985 (2020).
Tieleman, T., & Hinton, G.,"Divide the gradient by a running average of its recent magnitude". COURSERA: Neural Networks for Machine Learning, Lecture 6.5-rmsprop,2012.
Kingma, D. P., & Ba, J.,“Adam: A method for stochastic optimization". arXiv:1412.6980, (2014).
Tian, L. G., Liu, C., Liu, Y., Li, M., Zhang, J. Y., & Duan, H. L., "Research on plant diseases and insect pests identification based on CNN." In IOP Conference Series: Earth and Environmental Science, vol. 594, no. 1, p. 012009. IOP Publishing, 2020.
Wala’a, N. J., & Rana J. M., (2021). A Survey on Segmentation Techniques for Image Processing, Iraqi Journal for Electrical and Electronic Engineering, vol. 17 , pp. 73-93.
Saddam, Saba Abdual Wahid., (2022). "Wind Sounds Classification Using Different Audio Feature Extraction Techniques." Informatica 45, no. 7
Wala’a, N. J. & Esra J. H., (2019), Recognition Normal and Abnormal Human Activities by Implementation k-Nearest Neighbor and Decision Tree Models, Journal of Theoretical Applied information Technology, vol. 96, pp 6423-6443.
This work is licensed under a Creative Commons Attribution 3.0 License.