CONSTRUCTION OF A NEURAL NETWORK FOR PROBLEMS OF PREDICTION OF DISEASES OF AGRICULTURAL PLANTS
Keywords:
neural network, model, corn diseases, recognition, learning, prediction.Abstract
This article addresses the question of how to manage neural network training in Tensorflow. To analyze the accuracy of the constructed model, a database of diseased and healthy maize leaves was used. The model was analyzed for different training epochs. ROC curves of plant diseases were constructed and an error matrix was obtained.
References
1. Ranum, P., Peña-Rosas, J. P., and Garcia-Casal, M. N. (2014). Global maize production, utilization, and consumption. Ann. N.Y. Acad. Sci. 1312, 105–112. doi: 10.1111/nyas.12396
2. PubMed Abstract | CrossRef Full Text | Google Scholar
3. Savary, S., Ficke, A., Aubertot, J.-N., and Hollier, C. (2012). Crop losses due to diseases and their implications for global food production losses and food security. Food Security 4, 519–537. doi: 10.1007/s12571-012-0200-5.CrossRef Full Text | Google Scholar
4. Ouppaphan, P. (2017). “Corn disease identification from leaf images using convolutional neural networks,” in Proceedings of the 2017 21st International Computer Science and Engineering Conference (ICSEC), (Piscataway, NJ: IEEE), 1–5. doi: 10.1109/ICSEC.2017.8443919.CrossRef Full Text | Google Scholar
5. Saleem, M. H., Potgieter, J., and Mahmood Arif, K. (2019). Plant disease detection and classification by deep learning. Plants 8:468. doi: 10.3390/plants8110468
6. PubMed Abstract | CrossRef Full Text | Google Scholar
7. Panigrahi, K. P., Das, H., Sahoo, A. K., and Moharana, S. C. (2020). “Maize leaf disease detection and classification using machine learning algorithms,” in Progress in Computing, Analytics and Networking, eds P. K. Pattnaik, S. S. Rautaray, H. Das, and J. Nayak (Cham: Springer), 659–669. doi: 10.1155/2022/6504616. PubMed Abstract | CrossRef Full Text | Google Scholar
8. Liu, J., and Wang, X. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods 17, 1–18. doi: 10.1186/s13007-021-00722-9.PubMed Abstract | CrossRef Full Text | Google Scholar
9. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25, 1097–1105. Google Scholar
1. Dhaka, V. S., Meena, S. V., Rani, G., Sinwar, D., Kavita, Ijaz, M. F., et al. (2021). A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors 21:4749. doi: 10.3390/s21144749.PubMed Abstract | CrossRef Full Text | Google Scholar
10. DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., et al. (2017). Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107, 1426–1432. doi: 10.1094/PHYTO-11-16-0417-R PubMed Abstract | CrossRef Full Text | Google Scholar
11. Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., and Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Comput. Applic. 31, 8887–8895. doi: 10.1007/s00521-019-04228-3. CrossRef Full Text | Google Scholar
