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Abstract
Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture.
Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops.
Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.
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References
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References
A Sabu, K Sreekumar (2017), “Literature review of image features and classifiers used in leaf based plant recognition through image analysis approachâ€, In Inventive Communication and Computational Technologies (ICICCT), 2017, International Conference. pp. 145-149. https://doi.org/10.1109/ICICCT.2017.7975176 DOI: https://doi.org/10.1109/ICICCT.2017.7975176
Çaglar Küçük, Gül¸ sen Ta¸ skın, and Esra Erten (2016), “Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Imagesâ€, IEEE Journal of selected topics in applied earth observations and remote sensing, 9(6), pp 2509-2519. https://doi.org/10.1109/JSTARS.2016.2547843 DOI: https://doi.org/10.1109/JSTARS.2016.2547843
Dey N (2016), Classification and clustering in biomedical signal processing. IGI Global. https://doi.org/10.4018/978-1-5225-0140-4 DOI: https://doi.org/10.4018/978-1-5225-0140-4
G Li, Z Ma H Wang, (2016), “Development of a single leaf disease severity automatic grading system based on image processingâ€, In proceeding of the 2012 international conference on information technology and software engineering, Springer, Heidelberg, pp 665-675. https://doi.org/10.1007/978-3-642-34531-9_70 DOI: https://doi.org/10.1007/978-3-642-34531-9_70
Konstantios P Ferentinos (2018), “Deep learning models for plant disease detection and diagnosisâ€, Computer And Electronics in Agriculture, Elsevier, 145, pp 311- 318. https://doi.org/10.1016/j.compag.2018.01.009 DOI: https://doi.org/10.1016/j.compag.2018.01.009
M Islam, A Dinh, K Wahid, and P Bhowmik (2017), “Detection of potato diseases using image segmentation and multiclass support vector machineâ€, In Electrical and Computer Engineering (CCECE), 2017, IEEE 30th Canadian Conference, pp. 1-4. https://doi.org/10.1109/CCECE.2017.7946594 DOI: https://doi.org/10.1109/CCECE.2017.7946594
S Shrivastava, S K Singh, D S Hooda (2015), “Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimationâ€, Multimed tools application,74, pp 11484- 11484. https://doi.org/10.1007/s11042-014-2239-0 DOI: https://doi.org/10.1007/s11042-014-2239-0
Sharda P. Mohanty, David P. Huges and Marcel Salathe (2016), “Using Deep Learning for Plant Disease Detectionâ€, Forntiers in plant science, 7 (1419). https://doi.org/10.3389/fpls.2016.01419 DOI: https://doi.org/10.3389/fpls.2016.01419
Sukhvir Kaur, Shreelekha Pandey, and Shivani Goel (2018), “Semi-automatic leaf disease detection and classification system for soybean culture†IET Image Process, 12(6), pp 1038-1048. https://doi.org/10.1049/iet-ipr.2017.0822 DOI: https://doi.org/10.1049/iet-ipr.2017.0822
T Truong, A Dinh, and K Wahid (2017), “An IoT environmental data collection system for fungal detection in crop fieldsâ€, In Electrical and Computer Engineering (CCECE), 2017 IEEE 30th Canadian Conference, pp. 1-4. https://doi.org/10.1109/CCECE.2017.7946787 DOI: https://doi.org/10.1109/CCECE.2017.7946787
V Singh, A K Misra (2017), “Detection of plant leaf diseases using image segmentation and soft computing techniquesâ€, Information Processing in Agriculture, science direct, 4(1), pp 41-9. https://doi.org/10.1016/j.inpa.2016.10.005 DOI: https://doi.org/10.1016/j.inpa.2016.10.005
Vapnik V N, Vapnik V (1998), Statistical learning theory, Wiley, New York, vol 1.
Singh, G., Kumar, G., Bhatnagar, V., Srivastava, A., & Jyoti, K. (2019). POLLUTION MANAGEMENT THROUGH INTERNET OF THINGS: A SUBSTANTIAL SOLUTION FOR SOCIETY. Humanities & Social Sciences Reviews, 7(5), 1231-1237. https://doi.org/10.18510/hssr.2019.75162 DOI: https://doi.org/10.18510/hssr.2019.75162