| Autor: |
Sathiya, V., Josephine, M. S., Jeyabalaraja, V. |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 2 (2023); 153-163 |
| ISSN: |
2147-6799 |
| Popis: |
The main hub for the Indian economy is agriculture, which shares a great part of the gross domestic product, and nearly 70% of the people rely on it. Identification of proper medicinal plants that go into medicine formation is essential in the medicinal sector. Plant disease identification plays an essential part in taking the control measures for disease and developing the quantity and quality of the crop yield. The automatic disease identification in plants from their leaves is one of the most challenging tasks for researchers. The diseases among plants degrade their performance and result in a huge decrease in agricultural products. Plant disease automation is very much advantageous as it decreases the supervision work in big farms. The leaves being the plant’s food source, the accurate and early detection of leaf disease is essential. This study proposes a convolutional neural network approach that automates the identification of Basil and Mint leaf diseases. The advancement in CNN has changed the way of image processing compared to traditional techniques of image processing. This study has used the Inception V3 model for classification and to identify the types of diseases that occurred in Basil and Mint plants. The model was compiled using Adam Optimizer. The results of the study generated a validation accuracy of 77.55% for Basil leaves and an accuracy of 70.89% for mint leaves |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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