과제정보
This work was supported by 2021 project of the 14th Five Year Plan of Educational Science in Heilongjiang Province (No. GJB1421224 and GJB1421226), and the 2021 smart campus project of agricultural college branch of CAET (No. C21ZD02).
참고문헌
- Food and Agriculture Organization of the United Nations, "New standards to curb the global spread of plant pests and diseases," 2019 [Online]. Available: https://www.fao.org/news/story/en/item/1187738/icode.
- M. B. Tahir, M. A. Khan, K. Javed, S. Kadry, Y. D. Zhang, T. Akram, and M. Nazir, M. (2021). Recognition of apple leaf diseases using deep learning and variances-controlled features reduction," Microprocessors and Microsystems, vol. 2021, article no. 104027, 2021. https://doi.org/10.1016/j.micpro.2021.104027
- S. Chakraborty, S. Paul, and M. Rahat-uz-Zaman, "Prediction of apple leaf diseases using multiclass support vector machine," in Proceedings of 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 2021, pp. 147-151.
- S. R. N. M. Ayyub and A. Manjramkar, "Fruit disease classification and identification using image processing," in Proceedings of 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 754-758.
- G. M. James and S. Sujatha, "Categorising apple fruit diseases employing hybrid neural clustering classifier," Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2020.12.139
- S. Pandiyan, M. Ashwin, R. Manikandan, K. M. Karthick Raghunath, and G. R. Anantah Raman, "Heterogeneous Internet of Things organization predictive analysis platform for apple leaf diseases recognition," Computer Communications, vol. 154, pp. 99-110, 2020. https://doi.org/10.1016/j.comcom.2020.02.054
- C. Zhou, J. Song, S. Zhou, Z. Zhang, and J. Xing, "COVID-19 detection based on image regrouping and ResNet-SVM using chest X-ray images," IEEE Access, vol. 9, pp. 81902-81912, 2021. https://doi.org/10.1109/ACCESS.2021.3086229
- P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, vol. 7, pp. 59069-59080, 2019. https://doi.org/10.1109/access.2019.2914929
- H. J. Yu and C. H. Son, "Leaf spot attention network for apple leaf disease identification," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, 2020 pp. 52-53.
- X. Li and L. Rai, "Apple leaf disease identification and classification using ResNet models," in Proceedings of 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), Shenzhen, China, 2020, pp. 738-742.
- Y. Nagaraju, S. Swetha, and S. Stalin, "Apple and grape leaf diseases classification using transfer learning via fine-tuned classifier," in Proceedings of 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), Hyderabad, India, 2020, pp. 1-6.
- M. Agarwal, R. K. Kaliyar, G. Singal, and S. K. Gupta, "FCNN-LDA: a faster convolution neural network model for leaf disease identification on Apple's leaf dataset," in Proceedings of 2019 12th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, 2019, pp. 246-251.
- A. Gargade and S. A. Khandekar, "A review: custard apple leaf parameter analysis and leaf disease detection using digital image processing," in Proceedings of 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 267-271.
- C. Zhou, S. Zhou, J. Xing, and J. Song, "Tomato leaf disease identification by restructured deep residual dense network," IEEE Access, vol. 9, pp. 28822-28831, 2021. https://doi.org/10.1109/ACCESS.2021.3058947
- N. R. Bhimte and V. R. Thool, "Diseases detection of cotton leaf spot using image processing and SVM classifier," in Proceedings of 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 340-344.
- M. K. Maid and R. R. Deshmukh, "Statistical analysis of WLR (wheat leaf rust) disease using ASD FieldSpec4 spectroradiometer," in Proceedings of 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2018, pp. 1398-1402.
- F. Liu and Z. Xiao, "Disease spots identification of potato leaves in hyperspectral based on locally adaptive 1D-CNN," in Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2020, pp. 355-358.
- B. Liu, C. Tan, S. Li, J. He, and H. Wang, "A data augmentation method based on generative adversarial networks for grape leaf disease identification," IEEE Access, vol. 8, pp. 102188-102198, 2020. https://doi.org/10.1109/access.2020.2998839
- H. Sabrol and S. Kumar, "Plant leaf disease detection using adaptive neuro-fuzzy classification," in Advances in Computer Vision. Cham, Switzerland: Springer, 2019, pp. 434-443.
- C. Zhou, Z. Zhang, S. Zhou, J. Xing, Q. Wu, and J. Song, "Grape leaf spot identification under limited samples by fine grained-GAN," IEEE Access, vol. 9, pp. 100480-100489, 2021. https://doi.org/10.1109/ACCESS.2021.3097050
- Q. Zeng, X. Ma, B. Cheng, E. Zhou, and W. Pang, "Gans-based data augmentation for citrus disease severity detection using deep learning,' IEEE Access, vol. 8, pp. 172882-172891, 2020. https://doi.org/10.1109/access.2020.3025196
- J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and F. F. Li, "ImageNet: a large-scale hierarchical image database," in Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 248-255.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105, 2012.
- T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, "Microsoft COCO: common objects in context," in Computer Vision - ECCV 2014. Cham, Switzerland: Springer, 2014, pp. 740-755.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Anguelov, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 1-9.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
- AI_Challenger_2018 [Online]. Available: https://github.com/AIChallenger/AI_Challenger_2018.
- V. N. Vapnik, Statistical Learning Theory. New York, NY: John Wiley & Sons, 1998.
- A. C. Enache and V. Sgarciu, "Anomaly intrusions detection based on support vector machines with an improved bat algorithm," in Proceedings of 2015 20th International Conference on Control Systems and Computer Science, Bucharest, Romania, 2015, pp. 317-321.
- M. Erisoglu, N. Calis, and S. Sakallioglu, "A new algorithm for initial cluster centers in k-means algorithm," Pattern Recognition Letters, vol. 32, no. 14, pp. 1701-1705, 2011. https://doi.org/10.1016/j.patrec.2011.07.011