Crop Disease Prediction using Deep Learning Techniques - A Review
|International Journal of Computer Science and Engineering|
|© 2022 by SSRG - IJCSE Journal|
|Volume 9 Issue 4|
|Year of Publication : 2022|
|Authors : Gargi Sharma, Gourav Shrivastava|
How to Cite?
Gargi Sharma, Gourav Shrivastava, "Crop Disease Prediction using Deep Learning Techniques - A Review," SSRG International Journal of Computer Science and Engineering , vol. 9, no. 4, pp. 23-28, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I4P104
In agriculture, AI is bringing about a revolution by replacing traditional methods with more efficient ones and contributing to a better world. Artificial Intelligence and machine learning enable the development and implementation of devices that can identify and control plants, weeds, pests, and diseases through remote sensing. Plant disease lowers the quantity and quality of food, fiber, and biofuel crops, important to the Indian economy. In addition to reducing waste, using Deep learning technologies can increase quality and speed up market access for farmers. Here, we summarize recent crop disease detection research papers. Multiple deep learning algorithms demonstrate the current solutions for different crop disease diagnoses in this research. I hope this report will be useful to other crop disease detection researchers.
Crop Disease, CNN, Deep Learning.
 C. Zhou, S. Zhou, J. Xing, and J. Song, Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network, in IEEE Access, 9 (2021) 28822-28831. Available: 10.1109/ACCESS.2021.3058947.
 Y. Zhang, C. Song, and D. Zhang, Deep Learning-Based Object Detection Improvement for Tomato Disease, in IEEE Access, 8 (2020) 56607-56614. Available: 10.1109/ACCESS.2020.2982456.
 A. Khattak et al., Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model, in IEEE Access, 9 (2021) 112942-112954. Available: 10.1109/ACCESS.2021.3096895.
 Y. Ai, C. Sun, J. Tie and X. Cai, Research on Recognition Model of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments, in IEEE Access, 8 (2020) 171686-171693. [Online]. Available: 10.1109/ACCESS.2020.3025325.
 M. Ahmad, M. Abdullah, H. Moon, and D. Han, Plant Disease Detection in Imbalanced Datasets Using Efficient Convolutional Neural Networks with Stepwise Transfer Learning, in IEEE Access, 9 (2021) 140565-140580. doi: 10.1109/ACCESS.2021.3119655.
 D. Jiang, F. Li, Y. Yang, and S. Yu, A Tomato Leaf Diseases Classification Method Based on Deep Learning, Chinese Control and Decision Conference (CCDC), (2020) 1446-1450. 10.1109/CCDC49329.2020.9164457.
 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, in IEEE Access, 8 (2020) 102188-102198. [Online]. Available: 10.1109/ACCESS.2020.2998839.
 T. N. Pham, L. V. Tran, and S. V. T. Dao, Early Disease Classification of Mango Leave Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection, in IEEE Access, 8 (2020) 189960-189973. Available: 10.1109/ACCESS.2020.3031914.
 Wu, Q., Zhang, K. & Meng, J. Identification of Soybean Leaf Diseases via Deep Learning. J. Inst. Eng. India Ser. A 100 (2019) 659–666 (2019). https://doi.org/10.1007/s40030-019-00390- y
 Coulibaly, Solemane and Kamsu-Foguem, Bernard and Kamissoko, Dantouma and Traore, Daouda Deep neural networks with transfer learning in millet crop images. Computers in Industry, 108 (2019)115-120. ISSN 0166-3615
 Lin K, Gong L, Huang Y, Liu C and Pan J, Deep Learning-Based Segmentation and Quantification of Cucumber Powdery Mildew Using Convolutional Neural Network. Front. Plant Sci. 10 (2019) 155 [Online]. Available: 10.3389/fpls.2019.00155
 U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, ‘‘Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease, in IEEE Access, 7 (2019) 43721-43729. doi: 10.1109/ACCESS.2019.2907383.
 Luaibi, Ahmed & Salman, Tariq & Miry, Abbas., Detection of citrus leaf diseases using a deep learning technique. International Journal of Electrical and Computer Engineering. 11 (2021)1719- 1727. 10.11591/ijece. v11i2.pp1719-1727.
 Miaomiao Ji, Lei Zhang, Qiufeng Wu, Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks, Information Processing in Agriculture, 7(3) (2020).
 Mohanty SP, Hughes DP and Salathé M., Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci. 7 (2016) 1419. doi: 10.3389/fpls.2016.01419
 Liu J, Wang X. Plant diseases and pests detection based on deep learning: a review. Plant Methods. 2021 Feb 24;17(1) (2021) 22. doi: 10.1186/s13007-021-00722-9. PMID: 33627131; PMCID: PMC7903739.
 Hongkun Tian, Tianhai Wang, Yadong Liu, Xi Qiao, Yanzhou Li, Computer vision technology in agricultural automation —A review, Information Processing in Agriculture, 7(1) (2020) 1-19. ISSN 2214-3173,https://doi.org/10.1016/j.inpa.2019.09.006.
 P.SaleemandM.Arif, Plant disease detection and classification by deep learning, Plants, 8(11) (2019) 468.
 Y. Yang, K. Zheng, B. Wu, Y. Yang, and X. Wang, Network intrusion detection based on supervised adversarial variational auto-encoder with regularization, IEEE Access, 8 (2020) 42169– 42184.
 Y.Xian, S.Sharma, B.Schiele, and Z.Akata, F-VAEGAN-d2:A feature generating a framework for any-shot learning, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), (2019) 10275–10284.
 Alzubaidi, L., Zhang, J., Humaidi, A.J. etal. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data,8 (2021) 53.https://doi.org/10.1186/s40537-021-00444-8.
 Sarker, IH Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2 (2021) 420. https://doi.org/10.1007/s42979-021- 00815-1.
 A. Shrestha and A. Mahmood, Review of Deep Learning Algorithms and Architectures, in IEEE Access, 7 (2019) 53040- 53065. doi: 10.1109/ACCESS.2019.2912200