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About Plant Disease Detection Using AI Based VGG 16 Model
This document summarizes a study that used a VGG-16 convolutional neural network model to detect plant diseases using images. The study achieved an accuracy of 95.2% at detecting 19 different classes of plant diseases using over 15,000 images from the Plant Village dataset. The summary is: 1) Plant diseases can significantly impact crop yields, so timely detection is important for treatment. 2) Traditionally, farmers and scientists manually inspected plants, but this is time-consuming and requires expertise. 3) The study trained a VGG-16 CNN model on labeled images to classify 19 disease types, achieving 95.2% accuracy on test images.
Detailed Information
| Author: | ['Praveen J L'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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