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What is Different Stages of Watermelon Diseases Detection Using Optimized CNN about?

One of Saudi Arabia's biggest exports to its neighbors is fruits and vegetables. Some of its most productive crops include tomatoes, watermelons, grapes, oranges, onions, and citrus fruits. Watermelons and other fruits are prone to illness. A prompt and correct diagnosis of watermelon infections is necessary to ensure watermelon output. Identifying many stages of watermelon sickness in the environment is the aim of this study. Deep learning has been proposed as a potential tool for disease detection. In this paper, a deep learning approach is used to detect disease in watermelon plants. The convolutional neural network VGG-16 architecture was used in this study to recognize plant diseases and give farmers the tools they need to quickly treat afflicted plants. Unfortunately, because of a network over-fitting problem, the recall using the validation dataset was 0.7576 with the minimum score for true positives of 7%. The over-fitting issue is resolved in this study by contrasting two experiments. Different combinations of various hyperparameters make up each experiment. The initialization of the weights and the optimizer were the two key hyperparameters that were changed to enhance th

Author

Samah Alhazmi

Publisher

SPRINGER VERLAG, SINGAPOR

Published

2023

Language

EN

ISBN

9789811998584

Subjects

Engineering, Computer Science, Science