Abstract
In today’s world, crop diseases are one of the main threats to crop production and also to food safety. Disease detection using traditional methods that are not so accurate. Current phenotyping methods for plant disease are predominantly visual and are therefore slow and sensitive to human error and variation. Accuracy can be achieved using technologies such as artificial intelligence, IoT, algorithm based on rules, machine learning regression techniques, image processing, transfer learning, hyper-spectral imagery, leaf extraction and segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sarangdhar, A.A., Pawar, P.V.R., Blight, A.B.: Machine learning regression technique for using IoT. In: International Conference on Electronics, Communication and Aerospace Technology, pp. 449–454 (2017)
Morco, R.C., Bonilla, J.A., Corpuz, M.J.S., Angeles, J.M.: e-RICE: an expert system using rule-based algorithm to detect, diagnose, and prescribe control options for rice plant diseases in the Philippines. In: CSAI 2017, pp. 49–54 (2017)
Dutta, R., Smith, D., Shu, Y., Liu, Q., Doust, P., Heidrich, S.: Salad leaf disease detection using machine learning based hyper spectral sensing. In: IEEE SENSORS 2014 Proceedings, pp. 511–514 (2014)
Durmu, H., Olcay, E., Mürvet, K.Õ.Õ.: Disease detection on the leaves of the tomato plants by using deep learning. In: 6th International Conference on Agro-Geoinformatics (2017)
Ashourloo, D., Aghighi, H., Matkan, A.A., Mobasheri, M.R., Rad, A.M.: An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9, 4344–4351 (2016)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Ramcharan, A., Baranowski, K., Mccloskey, P., Legg, J., Hughes, D., Hughes, D.: Using transfer learning for ımage-based cassava disease detection. Frontiers (Boulder) 1–10 (2017)
Nagasubramanian, K., Jones, S., Sarkar, S., Singh, A.K.: Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean. Arxiv. 1–20 (2017)
Anantrasirichai, N., Hannuna, S., Canagarajah, N.: Automatic leaf extraction from outdoor images. Arxiv. 1–13 (2017)
Ramcharan, A., Mccloskey, P., Baranowski, K., Mbilinyi, N., Mrisho, L.: Assessing a mobile-based deep learning model for plant disease surveillance. Arxiv (2018)
Acknowledgements
The authors express gratitude towards the assistance provided by Accendere Knowledge Management Services Pvt. Ltd. In preparing the manuscripts. We also thank our mentors and faculty members who guided us throughout the research and helped us in achieving desired results.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
✓ All authors declare that there is no conflict of interest.
✓ No humans/animals involved in this research work.
✓ We have used our own data.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Parikshith, H., Naga Rajath, S.M., Pavan Kumar, S.P. (2020). Leaf Disease Detection Using Image Processing and Artificial Intelligence – A Survey. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-37218-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37217-0
Online ISBN: 978-3-030-37218-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)