Abstract
In growing nations like India, agriculture plays a vital role in the economy. Increase in agro-products affects the GDP of the nation to a good extent. To increase the productivity in agriculture, early detection of diseases needs to be identified and addressed. In the research work, we have concise our discussion with detection of crop diseases using machine learning techniques, especially with support vector machine (SVM) and artificial neural network (ANN). We have concluded our survey with the pros and cons of every method in context with input parameters (Crop type).
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References
Agarwal, A., Sarkar, A., Dubey, A.K.: Computer vision-based fruit disease detection and classification. In: Smart Innovations in Communication and Computational Sciences, pp. 105–115. Springer (2019)
Al-Khaffaf, H.S., Talib, A.Z., Abdul, R.: Salt and pepper noise removal from document images. In: International Visual Informatics Conference, pp. 607–618. Springer (2009)
Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricu. Eng. Int. CIGR J. 15(1), 211–217 (2013)
Badnakhe, M.R., Deshmukh, P.R.: Infected leaf analysis and comparison by otsu threshold and k-means clustering. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(3) (2012)
Bankar, S., Dube, A., Kadam, P., Deokule, S.: Plant disease detection techniques using canny edge detection & color histogram in image processing. Int. J. Comput. Sci. Inf. Technol. 52(2), 1165–1168 (2014)
Bashir, K., Rehman, M., Bari, M.: Detection and classification of rice diseases: an automated approach using textural features. Mehran University Res. J. Eng. Technol. 38(1), 239–250 (2019)
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Deokar, A., Pophale, A., Patil, S., Nazarkar, P., Mungase, S.: Plant disease identification using content based image retrieval techniques based on android system. Int. Adv. Res. J. Sci. Eng. Technol 3(2) (2016)
Deshapande, A.S., Giraddi, S.G., Karibasappa, K., Desai, S.D.: Fungal disease detection in maize leaves using haar wavelet features. In: Information and Communication Technology for Intelligent Systems, pp. 275–286. Springer (2019)
Dey, A., Bhoumik, D., Dey, K.N.: Automatic multi-class classification of beetle pest using statistical feature extraction and support vector machine. In: Emerging Technologies in Data Mining and Information Security, pp. 533–544. Springer (2019)
Dhingra, G., Kumar, V., Joshi, H.D.: A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135, 782–794 (2019)
Gaikwad, V.P., Musande, V.: Wheat disease detection using image processing. In: 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp. 110–112. IEEE (2017)
Gandhi, R., Nimbalkar, S., Yelamanchili, N., Ponkshe, S.: Plant disease detection using CNNS and GANS as an augmentative approach. In: 2018 IEEE International Conference on Innovative Research and Development (ICIRD), pp. 1–5. IEEE (2018)
Gavhale, K.R., Gawande, U., Hajari, K.O.: Unhealthy region of citrus leaf detection using image processing techniques. In: 2014 International Conference for Convergence of Technology (I2CT), pp. 1–6. IEEE (2014)
Ghaiwat, S.N., Arora, P.: Detection and classification of plant leaf diseases using image processing techniques: a review. Int. J. Recent Adv. Eng. Technol. 2(3), 1–7 (2014)
Husin, Z.B., Shakaff, A.Y.B.M., Aziz, A.H.B.A., Farook, R.B.S.M.: Feasibility study on plant chili disease detection using image processing techniques. In: 2012 Third International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 291–296. IEEE (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
KumarPatidar, P., Lalit, L., Singh, B., Bagaria, G.: Image filtering using linear and non linear filter for Gaussian noise. Int. J. Comput. Appl. 93(8), 29–34 (2014)
Lippmann, R.: An introduction to computing with neural nets. IEEE Assp Mag. 4(2), 4–22 (1987)
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Iniyan, S., Jebakumar, R., Mangalraj, P., Mohit, M., Nanda, A. (2020). Plant Disease Identification and Detection Using Support Vector Machines and Artificial Neural Networks. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_2
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