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About Deep Learning Based Classification On Fruit Diseases An Application For Precision Agriculture
This document discusses using deep learning techniques to classify fruit diseases for precision agriculture. Specifically, it proposes using a combined deep neural network and contour feature approach. A pretrained VGG19 model is fine-tuned on a plant dataset to extract useful features. Contour features are also extracted using PHOG and combined with the deep features. A relevance-based optimization technique then selects the best features for final classification. This combined approach achieved up to 99.6% accuracy on the proposed method, outperforming previous techniques. The accurate and early detection of fruit diseases using this method can help reduce economic losses from disease spread and increase treatment effectiveness in agriculture.
Detailed Information
| Author: | ['Juan Palacios'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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