Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey
DOI:
https://doi.org/10.4108/eetiot.4595Keywords:
Plant diseases, Transfer learning, Densenet, DN, Efficientnet, EN, Convolutional Neural Network, CNN, Resnet, RNAbstract
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.
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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
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.
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.
D. Hammerstrom, "Working with neural networks," in IEEE Spectrum, vol. 30, no. 7, pp. 46-53, July 1993, doi: 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
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.
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.
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.
PallapothalaTejaswini et al 2022 IOP Conf. Ser.: Earth Environ. Sci. 1032 012017 DOI 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
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
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
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.
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
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
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
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
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.
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