A Review on Plant Leaf Disease Detection

Authors

  • Jayashree R Department of Studies and Research in Computer Applications, Tumkur University, Tumkur, Karnataka, India
  • Kusuma Kumari B M Department of Studies and Research in Computer Applications, Tumkur University, Tumkur, Karnataka, India

DOI:

https://doi.org/10.5281/zenodo.10444452

Keywords:

Image processing, machine learning, deep learning techniques

Abstract

Even though there is a rapid increase in the world’s population, agriculture provides food to all the human beings. Since it is essential to cater food to overall population, it is recommended to predict plant leaf diseases at their early stages. This paper presents a survey on plant leaf disease detection using various, image processing techniques, machine learning and deep learning techniques and also various algorithms can be used for identification and classification of plant leaf diseases in plant. This paper compares and contrast different techniques used by different researcher to identify plant leaf disease

References

Zamani, A. S., Anand, L., Rane, K. P., Prabhu, P., Buttar, A. M., Pallathadka, H., ... & Dugbakie, B. N. (2022). Performance of machine learning and image processing in plant leaf disease detection. Journal of Food Quality, 2022, 1-7.

Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., & Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14), 6982.

Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3(1), 305-310.

Vallabhajosyula, S., Sistla, V., & Kolli, V. K. K. (2022). Transfer learning-based deep ensemble neural network for plant leaf disease detection. Journal of Plant Diseases and Protection, 129(3), 545-558.

Singh, A. K., Sreenivasu, S. V. N., Mahalaxmi, U. S. B. K., Sharma, H., Patil, D. D., & Asenso, E. (2022). Hybrid feature-based disease detection in plant leaf using convolutional neural network, bayesian optimized SVM, and random forest classifier. Journal of Food Quality, 2022, 1-16.

Wu, Y., Feng, X., & Chen, G. (2022). Plant leaf diseases fine-grained categorization using convolutional neural networks. IEEE Access, 10, 41087-41096.

Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102.

Tamilvizhi, T., Surendran, R., Anbazhagan, K., & Rajkumar, K. (2022). Quantum behaved particle swarm optimization-based deep transfer learning model for sugarcane leaf disease detection and classification. Mathematical Problems in Engineering, 2022.

Ansari, A. S., Jawarneh, M., Ritonga, M., Jamwal, P., Mohammadi, M. S., Veluri, R. K., ... & Shah, M. A. (2022). Improved support vector machine and image processing enabled methodology for detection and classification of grape leaf disease. Journal of Food Quality, 2022.

Padma, U., Jagadish, S., & Singh, M. K. (2022). Recognition of plant’s leaf infection by image processing approach. Materials Today: Proceedings, 51, 914-917.

Raut, S., & Ingole, K. (2017). Review on leaf disease detection using image processing techniques. International Research Journal of Engineering and Technology (IRJET), 4(04), 2044-2047.

Ahmed, S. T., Ashwini, S., Divya, C., Shetty, M., Anderi, P., & Singh, A. K. (2018). A hybrid and optimized resource scheduling technique using map reduce for larger instruction sets. International Journal of Engineering & Technology, 7(2.33), 843-846.

Ahmed, S. T., Basha, S. M., Arumugam, S. R., & Kodabagi, M. M. (2021). Pattern Recognition: An Introduction. MileStone Research Publications

Downloads

Published

2023-12-30

How to Cite

Jayashree R, & Kusuma Kumari B M. (2023). A Review on Plant Leaf Disease Detection . International Journal of Human Computations & Intelligence, 2(6), 275–279. https://doi.org/10.5281/zenodo.10444452

Issue

Section

Survey / Literature Reviews