ISSN : 2583-2646

Survey on Disease Identification and Severity Level Estimation on Plant

ESP Journal of Engineering & Technology Advancements
© 2023 by ESP JETA
Volume 3  Issue 1
Year of Publication : 2023
Authors : Supriya Bhosale, Aditi Chhabria
:10.56472/25832646/JETA-V3I2P103

Citation:

Supriya Bhosale, Aditi Chhabria, 2023. "Survey on Disease Identification and Severity Level Estimation on Plant" ESP Journal of Engineering & Technology Advancements  3(1): 87-91.

Abstract:

The most nutritive crop that is being cultivated across the globe is the tomato plant. Moreover, it has a vital impact on the growth of the agricultural economy in terms of cultivation and export levels Plants not only contains protein, but also has pharmacological properties that safeguard the people from conditions like “high blood pressure, hepatitis, gingival bleeding”, etc. Nowadays, they are utilized in a large- scale, and as a consequence of this, the market for plants is also getting increased. Statistics reveal that the small producers produce more than 80 percent of plant and therefore, the economic losses are more than 50 percent due to the insects and pathogens. The primary issues affecting the plants development are pathogens and insect pests, so researching the detection of crop diseases is especially important. The management of plants diseases is indeed a difficult process that requires constant care during the growing season and is responsible for the substantial fraction of overall production level. Earlier identification could significantly minimize the treatment costs, mitigate the severity of chemical contaminants, and alleviate the chances of yield loss. Present methods of disease diagnosis are restricted in terms of time required for qualified technicians to physically identify and evaluate the pathogens, exacerbated by the number of plants in commercial greenhouses and the small scale of indications at the early stage of disease. Usually, the cost and complexity involved in disease detection restricts the outbreak exploration to an occasional cycle or limited sampling. Molecular processing, spectroscopy, and examination of volatile organic compounds have been used in the studies of the automatic detection processes. Though, they are costly and inefficient to implement on a real - time operating scale.

References:

[1] Q. Wu, Y. Chen and J. Meng, "DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification," in IEEE Access, vol. 8, pp. 98716-98728, 2020, doi: 10.1109/ACCESS.2020.2997001.
[2] G. Yang, G. Chen, Y. He, Z. Yan, Y. Guo and J. Ding, "Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases," in IEEE Access, vol. 8, pp. 211912-211923, 2020, doi: 10.1109/ACCESS.2020.3039345.
[3] Y. Zhang, C. Song and D. Zhang, "Deep Learning-Based Object Detection Improvement for Tomato Disease," in IEEE Access, vol. 8, pp. 56607-56614, 2020, doi: 10.1109/ACCESS.2020.2982456.
[4] N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad and S. Berman, "Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus," in IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 354-360, Jan. 2016, doi: 10.1109/LRA.2016.2518214.
[5] Patrick Wspanialy,Medhat Moussa,"A detection and severity estimation system for generic diseases of tomato greenhouse plants", Computers and Electronics in Agriculture,2020
[6] Joaquín Cañadas,Jorge Antonio,Sánchez-Molina,Francisco,Rodríguez,Isabel Maríadel Águila,"Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes", Information Processing in Agriculture, Vol.4, No.1, March 2017
[7] Karthik R., Hariharan M., Sundar Anand, Priyanka Mathikshara, Annie Johnson, MenakaR.,"Attention embedded residual CNN for disease detection in tomato leaves", Applied Soft Computing, Vol.86, January 2020
[8] Xiao Chen, Guoxiong Zhou, Aibin Chen, Jizheng Yi, Wenzhuo Zhang, YahuiHu,"Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet", Computers and Electronics in Agriculture, Vol. 178, November 2020
[9] C. Zhou, S. Zhou, J. Xing and J. Song, "Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network," in IEEE Access. doi: 10.1109/ACCESS.2021.3058947
[10] Q. Wu, Y. Chen and J. Meng, "DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification," in IEEE Access, vol. 8, pp. 98716-98728, 2020. doi: 10.1109/ACCESS.2020.2997001
[11] G. Yang, G. Chen, Y. He, Z. Yan, Y. Guo and J. Ding, "Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases,"in IEEEAccess,vol.8,pp.211912-211923,2020.doi: 10.1109/ACCESS.2020.3039345
[12] Y. Zhang, C. Song and D. Zhang, "Deep Learning-Based Object Detection Improvement for Tomato Disease," in IEEE Access, vol. 8, pp. 56607-56614, 2020.doi: 10.1109/ACCESS.2020.2982456
[13] N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad and S. Berman, "Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus," in IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 354-360, Jan. 2016. doi: 10.1109/LRA.2016.2518214
[14] Sangeeta, Punam Ranjan, Achuit K. Singh, "Two distinct monopartite begomovirus-betasatellite complexes in western India cause tomato leaf curl disease", Virus Research, 2021
[15] Ting Shen,Yunhui Lei,Yuhui Du,"Identification and application of Streptomyces microflavus G33 in compost to suppress tomato bacterial wilt disease", Applied Soil Ecology, 2020
[16] Shreya M. Joshi,Savitha De Britto,Sudisha Jogaiah,"Myco-engineered selenium nanoparticles elicit resistance against tomato late blight disease by regulating differential expression of cellular, biochemical and defense responsive genes", Journal of Biotechnology, 2020
[17] Victor Gonzalez-Huitron,José A. León-Borges,Hector Rodriguez,"Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4", Computers and Electronics in Agriculture, 2021
[18] Humaira Rizwana,Najat A. Bokahri,Horiah A. Aldehaish,"Postharvest disease management of Alternaria spots on tomato fruit by Annona muricata fruit extracts", Saudi Journal of Biological Sciences, 2021
[19] Giti Alizadeh-Moghaddam,Zahra Rezayatmand,Mahdi Khozaei,"Bio-genetic analysis of resistance in tomato to early blight disease, Alternaria alternata", Phytochemistry, 2020
[20] Tavga Sulaiman Rashid, "Bioactive metabolites from tomato endophytic fungi with antibacterial activity against tomato bacterial spot disease", Rhizosphere, 2020
[21] S. Dhakshina Kumar,S. Esakkirajan,B. Keerthi Veena,"Design of disease prediction method based on whale optimization employed artificial neural network in tomato fruits", Materials Today: Proceedings, 2020
[22] Shixue Zhao,Yanhua Guo,Bang An,"Expression of flagellin at yeast surface increases biocontrol efficiency of yeast cells against postharvest disease of tomato caused by Botrytis cinerea", Postharvest Biology and Technology, 2019
[23] Xuhui Deng,Na Zhang,Qirong Shen,"Rhizosphere bacteria assembly derived from fumigation and organic amendment triggers the direct and indirect suppression of tomato bacterial wilt disease", Applied Soil Ecology, 2019
[24] Saiqa Khan,Meera Narvekar,"Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment", Journal of King Saud University - Computer and Information Sciences, 2020
[25] G Brammya, S Praveena, N S Ninu Preetha, R Ramya, B R Rajakumar, D Binu,"Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm", The Computer Journal,2019

Keywords:

Automatic Detection Processes. Disease Detection, Real - Time Operating Scale.