Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey

Authors

DOI:

https://doi.org/10.4108/eetiot.4595

Keywords:

Plant diseases, Transfer learning, Densenet, DN, Efficientnet, EN, Convolutional Neural Network, CNN, Resnet, RN

Abstract

Agriculture plays an important role towards the economic growth of any nation. It also has a significant effect on global GDP. The enhancement in agro production helps in controlling greatly the inflation. Today a large percentage of population from rural India is still dependent on agriculture. But every year there is a huge loss happen in agriculture due to different plant diseases. A farmer does not able to recognise any plant disease at its beginning stage due to insufficient knowledge. Sometimes they take help of agriculture officers in this process.  However, if the infection level has grown by that point, it typically leads to a significant crop loss. Also the diagnosis made by the agriculture officer based on their past experience, is always not accurate.  Computational vision-based solutions can be used to deal with this great disaster to a large extent. Computer vision mainly deals with different algorithms that enable a computer to identify a hidden pattern for recognition using image or video data. In this work a detailed investigation has been performed on the different computer vision based solutions proposed by different authors to detect various crop diseases.

Downloads

Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">

References

https://www.worldbank.org/en/topic/agriculture/overview

https://www.ibef.org/industry/agriculture-india

https://reliefweb.int/disaster/ce-2022-000199

https://www.indiatimes.com/explainers/news/

Bhoomika, S.S., Poornima, K.M. (2023). Plant Leaf Disease Detection and Classification Using Deep Learning Technique. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_7 DOI: https://doi.org/10.1007/978-981-19-4863-3_7

Vijai Singh, A.K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Information Processing in Agriculture, Volume 4, Issue 1, 2017, Pages 41-49, ISSN 2214-3173, https://doi.org/10.1016/j.inpa.2016.10.005. DOI: https://doi.org/10.1016/j.inpa.2016.10.005

X. Li and Y. Shi, "Computer Vision Imaging Based on Artificial Intelligence," 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Hunan, China, 2018, pp. 22-25, doi: 10.1109/ICVRIS.2018.00014. DOI: https://doi.org/10.1109/ICVRIS.2018.00014

D. Hammerstrom, "Working with neural networks," in IEEE Spectrum, vol. 30, no. 7, pp. 46-53, July 1993, doi: 10.1109/6.222230 DOI: https://doi.org/10.1109/6.222230

J., Andrew, Jennifer Eunice, Daniela Elena Popescu, M. KalpanaChowdary, and Jude Hemanth. 2022. "Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications" Agronomy 12, no. 10: 2395. https://doi.org/10.3390/agronomy12102395 DOI: https://doi.org/10.3390/agronomy12102395

Soo Jun Wei, Dimas Firmanda Al Riza, HermawanNugroho, "Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification", Journal of Agriculture and Food Research, Volume 10,2022,100389, ISSN 2666-1543, https://doi.org/10.1016/j.jafr.2022.100389. DOI: https://doi.org/10.1016/j.jafr.2022.100389

Sunil S. Harakannanavar, Jayashri M. Rudagi, Veena I Puranikmath, Ayesha Siddiqua, R Pramodhini, “Plant leaf disease detection using computer vision and machine learning algorithms”, Global Transitions Proceedings, Volume 3, Issue 1, 2022, Pages 305-310,ISSN 2666-285X, https://doi.org/10.1016/j.gltp.2022.03.016. DOI: https://doi.org/10.1016/j.gltp.2022.03.016

M. H. K. Mehedi et al., "Plant Leaf Disease Detection using Transfer Learning and Explainable AI," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0166-0170, doi: 10.1109/IEMCON56893.2022.9946513. DOI: https://doi.org/10.1109/IEMCON56893.2022.9946513

PallapothalaTejaswini et al 2022 IOP Conf. Ser.: Earth Environ. Sci. 1032 012017 DOI 10.1088/1755-1315/1032/1/012017 DOI: https://doi.org/10.1088/1755-1315/1032/1/012017

M., Azath, Zekiwos, MeleseBruck, Abey, 2021, "Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis", Journal of Electrical and Computer Engineering, Hindawi, 9981437, 2021, https://doi.org/10.1155/2021/998143710.1155/2021/9981437 DOI: https://doi.org/10.1155/2021/9981437

Hassan, S.M.; Maji, A.K.; Jasiński, M.; Leonowicz, Z.; Jasińska, E. “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach”. Electronics 2021, 10, 1388. https://doi.org/10.3390/electronics10121388 DOI: https://doi.org/10.3390/electronics10121388

Qi, H.; Liang, Y.; Ding, Q.; Zou, J. “Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble”. Appl. Sci. 2021, 11, 1950. https://doi.org/10.3390/app11041950 DOI: https://doi.org/10.3390/app11041950

M. P. Vaishnnave, K. S. Devi, P. Srinivasan and G. A. P. Jothi, "Detection and Classification of Groundnut Leaf Diseases using KNN classifier," 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-5, doi: 10.1109/ICSCAN.2019.8878733. DOI: https://doi.org/10.1109/ICSCAN.2019.8878733

R.Sangeetha, M. Mary Shanthi Rani, “Tomato Leaf Disease Prediction using Convolutional Neural Network”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online), Volume-9 Issue-1, November 2019, DOI: 10.35940/ijitee.L3776.119119 DOI: https://doi.org/10.35940/ijitee.L3776.119119

S. Ramesh et al., "Plant Disease Detection Using Machine Learning," 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India, 2018, pp. 41-45, doi: 10.1109/ICDI3C.2018.00017 DOI: https://doi.org/10.1109/ICDI3C.2018.00017

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470

G. P. Rout and S. N. Mohanty, "A Hybrid Approach for Network Intrusion Detection," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India, 2015, pp. 614-617, doi: 10.1109/CSNT.2015.76. DOI: https://doi.org/10.1109/CSNT.2015.76

Downloads

Published

13-12-2023

How to Cite

[1]
A. Bhilare, D. Swain, and N. Patel, “Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023.

Issue

Vol. 10 (2024): EAI Endorsed Transactions on Internet of Things

Section

Short communication

License

Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.