Plant disease prediction using convolutional neural network
Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity
Gokulnath, B. V., & Usha Devi, G. A survey on plant disease prediction using machine learning and deep learning techniques. Inteligencia Artificial, 22(63), 0-19, 2017.
Ashok, S., Kishore, G., Rajesh, V., Suchitra, S., Sophia, S. G., & Pavithra, B. Tomato Leaf Disease Detection Using Deep Learning Techniques. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 979-983). IEEE, 2020. DOI: https://doi.org/10.1109/ICCES48766.2020.9137986
Ferentinos, K. P. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318, 2018. DOI: https://doi.org/10.1016/j.compag.2018.01.009
Reddy, K. N., Polaiah, B., & Madhu, N. A literature survey: plant leaf diseases detection using image processing techniques. IOSR J. Electron. Commun. Eng., 2017. DOI: https://doi.org/10.9790/2834-1203021315
Lowe, A., Harrison, N., & French, A. P. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant methods, 13(1), 1-12, 2017. DOI: https://doi.org/10.1186/s13007-017-0233-z
Kanagaraj, S., Hema, M. S., & Gupta, M. N. Environmental Risk Factors and Parkinson‟ s Disease–A Study Report. International Journal of Recent Technology and Engineering (IJRTE) ISSN, 2277-3878 2019.
Yang, X., & Guo, T. Machine learning in plant disease research. European Journal of BioMedical Research, 3(1), 6-9, 2017. DOI: https://doi.org/10.18088/ejbmr.3.1.2017.pp6-9
Nagasubramanian, K., Jones, S., Singh, A. K., Sarkar, S., Singh, A., & Ganapathysubramanian, B. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant methods, 15(1), 1-10., 2019. DOI: https://doi.org/10.1186/s13007-019-0479-8
Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S., & de Albuquerque, V. H. C. Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits, Systems, and Signal Processing, 39(2), 818-836., 2020 DOI: https://doi.org/10.1007/s00034-019-01041-0
Hema, M. S., & Chandramathi, S. Federated query processing service in service oriented business intelligence. In International Conference on Advances in Communication, Network, and Computing (pp. 337-340). Springer, Berlin, Heidelberg, 2011. DOI: https://doi.org/10.1007/978-3-642-19542-6_62
Saleem, M. H., Potgieter, J., & Arif, K. M. Plant disease detection and classification by deep learning. Plants, 8(11), 468, 2019 DOI: https://doi.org/10.3390/plants8110468
Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2019). Solving current limitations of deep learning based approaches for plant disease detection. Symmetry, 11(7), 939, 2019. DOI: https://doi.org/10.3390/sym11070939
Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., & Vinod, P. V. Plant disease detection using machine learning. In 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C) (pp. 41-45). IEEE, 2018 DOI: https://doi.org/10.1109/ICDI3C.2018.00017
Dhingra, G., Kumar, V., & Joshi, H. D. Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15), 19951-20000, 2018. DOI: https://doi.org/10.1007/s11042-017-5445-8
Wang, G., Sun, Y., & Wang, J. Automatic image-based plant disease severity estimation using deep learning. Computational intelligence and neuroscience, 2017. DOI: https://doi.org/10.1155/2017/2917536
Prashanthi, V., & Srinivas, K. Plant disease detection using Convolutional neural networks. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 2020. DOI: https://doi.org/10.30534/ijatcse/2020/21932020
Ghosh, G., & Chakravarty, S. Grapes Leaf Disease Detection Using Convolutional Neural Network. International Journal of Modern Agriculture, 9(3), 1058-1068, 2020.
Vetal, S., & Khule, R. S. Tomato plant disease detection using image processing. International Journal of Advanced Research in Computer and Communication Engineering, 6(6), 293-297,2017. DOI: https://doi.org/10.17148/IJARCCE.2017.6651
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