| Autor: |
Mudgil, Rishabh, Garg, Nidhi, Sharma, Preeti, Madhu, Charu, Singh, Preeti |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3209 Issue 1, p1-8, 8p |
| Abstrakt: |
Despite significant advancements in scientific research and genetics aimed at enhancing agricultural product quality and quantity, we still witness decrease in the production of crops. Factors contributing to this include wars, ethnic conflicts, and particularly plant diseases that can devastate entire crops, significantly impacting agricultural production. Artificial intelligence and computer vision have emerged as transformative solutions to various problems. Employing deep learning through convolutional neural networks (CNNs), have emerged as powerful tools for detecting and classifying plant diseases. The proposed model is the combination of VGG16 and CNN. The proposed model is implemented in python using google colab. The investigated architecture is also evaluated by comparing it with a machine learning model and CNN model. The comparison between proposed and existing models is done based on accuracy, precision and recall. Comparative analysis between KNN, SVM, Random Forest, CNN and proposed has been carried out. The accuracy of 97.89 percent is achieved for the proposed model. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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