DOI : https://doi.org/10.62527/ijasce.4.2.83

Paddy Leaf Symptom-based Disease Classification Using Deep CNN with ResNet-50

Pushpa Athisaya Sakila Rani (1), N.Suresh Singh (2)
(1) Research Scholar, Department of Computer Science, Malankara Catholic College, India
(2) Associate Professor and Head, Department of Computer Applications, Malankara Catholic College, India
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How to cite (IJASEIT) :
Rani, P. A. S., & Singh, N. (2022). Paddy Leaf Symptom-based Disease Classification Using Deep CNN with ResNet-50. International Journal of Advanced Science Computing and Engineering, 4(2), 88–94. https://doi.org/10.62527/ijasce.4.2.83
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Agriculture is a globally important occupation. Food is a fundamental need for all living things on the earth, hence it plays a significant role. As a result, agricultural product quality has to be improved. Paddy is susceptible to a variety of illnesses, just like all other crops. Diseases vary by region and season. Despite the fact that the number of new technologies being implemented in agriculture is rapidly expanding, farmers in our nation continue to rely on old methods for disease detection. Machine learning relies heavily on features to classify images. The advancement of the deep convolutional neural network paves the path for disease detection in rice based on deep characteristics, with the expectation of excellent yields. Field photos of four forms of rice leaf diseases, including bacterial leaf blight, brown spot, leaf smut, and tungro, were introduced using this proposed method. The model is trained using the deep CNN classification technique. The pre-trained ResNet-50 model is also included to increase the model's prediction accuracy. Existing approaches are outperformed by the combination of Deep CNN and ResNet-50. Accuracy was used as a criterion for evaluating performance.

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