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About Grape Detection With Convolutional Neural N - 2020 - Expert Systems With Applica
This document discusses using convolutional neural networks to detect grapes in images. Specifically, it investigates 1) using different input features like color, grayscale, and color histograms, 2) the impact of network parameters and data augmentation, and 3) the performance of transfer learning with 11 pre-trained architectures. The results show that color images provide the best performance for grape detection. Using transfer learning with Resnet networks achieves 99% accuracy for detecting red and white grapes. Data augmentation, normalization, and input features significantly impact the classification results.
Detailed Information
| Author: | ['Michael Wondemu'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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