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CNN Architectures for Crop Disease Detection by zabursa7 is a document available to read on EtoBox.

What is CNN Architectures for Crop Disease Detection about?

This research compares the performance of three Convolutional Neural Network architectures—MobileNet, EfficientNet, and Inception—in predicting crop diseases using datasets of rice, corn, and potatoes. The findings indicate that EfficientNet achieves the highest accuracy across all datasets, while MobileNet excels in speed and efficiency, and Inception struggles with complex features. The study provides valuable insights for selecting appropriate CNN architectures based on specific application needs in agri

Author

zabursa7

Language

EN