Plant Disease Detection System for Agricultural Application in Cloud Using CNN
DOI:
https://doi.org/10.26438/ijcse/v6i12.246249Keywords:
Convolutional Neural Network (CNN), Cloud Computing,, Advanced Neural NetworkAbstract
Plants are cultivated for food, medicine, clothing, shelter, fiber, and beauty for thousands of years. Fungi, bacteria, and viruses are the causing source of plant disease. So, need of an Automatic detection of plant disease for this problem. A traditional method of plant disease detection is not efficient and also unreliable. Due to pest attack, nearly 18% of crop yield is lost in worldwide during every year. Identification of plant disease is difficult in manually but which is a key factor to preventing the losses. In existing, a module is applied in a farm, that contains large number of different sensors and also a device is used for converting and transfer data for monitoring and controlling purposes. And then Image processing is showing the disease visually. In this, we approach a Convolutional Neural Network (CNN) classification model deployed in a smart phone app and also responsible to predict the plant disease for dynamic plants image. This method is generic and useful. Frequently, we should adding and updating new diseases in the datasets and then cloud computing is used for storing, retrieving and serving data. Captured image of normal plants are stored in the cloud server, and these images are compared with the diseased plant leaves in the cloud campus. This paper presents a automated detection of various diseases associated with crops and also given a proposed methodology for computing amount of diseases in various crops.
References
[1] Nasir, Fakhri A. M. Nordin Rahman A., and A. Mamat Rasid. "A study of image processing in agriculture application under high performance computing environment." International Journal of Computer Science and Telecommunications 3, no. 8 (2012): 1624.
[2] Prasad, Shitala, K. Peddoju Sateesh, and Ghosh Debashis. "AgroMobile: a cloud-based framework for agriculturists on mobile platform." International Journal of advanced science and technology 59 (2013): 41-52
[3] Mohanty, Sharada, David P. Hughes, and Salathé Marcel, "Using deep learning for image-based plant disease detection." Frontiers in plant science 7 (2016): 1419.
[4] Radford, Alec, Metz uke, and Chintala Soumith. "Unsupervised representation learning with deep convolutional generative adversarial networks (2015)." arXiv preprint arXiv:1511.06434
[5] Yan Simon, Karen, and Zisserman Andrew. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[6] David Hughes, , and Salathé Marcel. "An open access repository of images on plant health to enable the development of mobile disease diagnostics." arXiv preprint arXiv:1511.08060 (2015).
[7] Szegedy, Christian, Vanhoucke Vincent, Ioffe Sergey, Shlens Jon, and Zbigniew Wojna. "Rethinking the inception architecture for computer vision." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826. 2016.
[8] Andrew Howard G., Zhu Menglong, Bo Chen, Dmitry Kalenichenko, Wang Weijun, Tobias Weyand, Andreetto Marco and Hartwig Adam. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[9] Giusti, Alessandro, Guzzi jérôme, Dan C. Ciresan, Fang-Lin He, Rodríguez Juan P, Flavio Fontana, Matthias Faessler et al. "A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots." IEEE Robotics and Automation Letters 1, no. 2 (2016): 661-667.
[10] Augustus Odena. "Semi-supervised learning with generative adversarial networks." arXiv preprint arXiv:1606.01583 (2016).
[11] Zhao, Fuqing, Zongyi Ren, Dongmei Yu, and Yahong. "Application of an improved particle swarm optimization algorithm for neural network training." In Neural Networks and Brain, 2005. ICNN&B'05. International Conference on, vol. 3, pp. 1693-1698. IEEE, 2005.
[12] Vinod Nair, and E. Hinton Geoffrey. "Rectified linear units improve restricted boltzmann machines." In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814. 2010.
[13] Yann LeCun, Patrick Haffner, Bottou Léon, and Yoshua Bengio. "Object recognition with gradient-based learning." In Shape, contour and grouping in computer vision, pp. 319-345. Springer, Berlin, Heidelberg, 1999.
[14] Kaibo Duan, , S. Keerthi Sathiya, Wei Chu, Krishnaj Shirish Shevade, Neow and Aun Poo. "Multi-category classification by soft-max combination of binary classifiers." In International Workshop on Multiple Classifier Systems, pp. 125-134. Springer, Berlin, Heidelberg, 2003.
[15] S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna Calculation of Texture Features for Polluted Leaves “International Journal of Scientific Research in Computer Sciences and Engineering” Vol.6 , Issue.1 , pp.11-21, Feb-2018
[16] Sakshi kathuria “A Survey on Security Provided by Multi-Clouds in Cloud Computing” International Journal of Scientific Research in Network Security and Communication, Vol.6 , Issue.1, pp.23-
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