Paddy Pathogens Classification: A Comparative Analysis of Deep Learning Optimizers

Main Article Content

Article Sidebar

Published Sep 8, 2021
malathi velu


The pathogens are the key element that leads to fewer yields of up to 16% globally, Pathogens are the biotic agents that cause diseases in crops such as viruses, bacteria, and fungus. Currently, crop disease is classified at an earlier stage by state-of-the-art deep learning techniques. The development of a computational approach for diagnosing diseases of the crops is an emerging research zone in precision agriculture. This research proposed a classification of paddy leaf diseases namely Bacteria leaf blight, blast, hispa, leaf spot, leaf folder based on deep learning techniques. The data set is collected directly from the agricultural field using the IoT device. ResNet-50 architecture is utilized to develop the neural network framework and, the features are extracted using the convolutional block. Seven different kinds of optimizers namely ADAM, SGD, RMSProp, Adagrad, Adamax, Nadam, and, Adadelta were analyzed then, interpretation was carried out based on processing time, classification accuracy and, error rate to recognize which model performs the best. Our research finding states that Adagrad optimizers produce better accuracy of 0.96 and less error of 0.19, then learning rate of the Adadelta is updated to 0.0001 and achieved an accuracy of 0.97 and loss of 0.14.

How to Cite

velu, malathi. (2021). Paddy Pathogens Classification: A Comparative Analysis of Deep Learning Optimizers. SPAST Abstracts, 1(01). Retrieved from
Abstract 2 |

Article Details

Halgamuge, M. N., Daminda, E., & Nirmalathas, A. (2020). Best optimizer selection for predicting bushfire occurrences using deep learning. Natural Hazards, 103(1), 845–860.
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018). Rainbow: Combining improvements in deep reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).
Janssens, C., Havlík, P., Krisztin, T., Baker, J., Frank, S., Hasegawa, T., Leclère, D., Ohrel, S., Ragnauth, S., & Schmid, E. (2020). Global hunger and climate change adaptation through international trade. Nature Climate Change, 10(9), 829–835.
Kingma, D. P., & Ba, J. (2015). Adam (2014), a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), ArXiv Preprint ArXiv, 1412.
GE3- Computers & Information Technology