Marigold Flower Disease Prediction through Deep Neural Network with Multimodal Image
How to Cite?
Sumitra Nuanmeesri, Shutchapol Chopvitayakun, Preedawon Kadmateekarun, Lap Poomhiran, "Marigold Flower Disease Prediction through Deep Neural Network with Multimodal Image," International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 174-180, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I7P224
This research aims to develop the convolutional neural network model to predict the marigold flower disease through multimodal image processing. The marigold flower was detected on the image dataset by applying the Fast Approximate Nearest neighbor Search Algorithm, centroids, and GrabCut algorithm. The image segmentation with the watershed and hue saturation value techniques were used to process the image dataset for the neural network modeling. The result showed that the developed model with the watershed dataset has the highest efficiency. The model gave the validation accuracy of 88.03%, the validation loss of 4.21%, and the accuracy of the model testing was 91.67%. Therefore, it could be said that image segmentation processing could optimize flower disease image classification in deep neural networks.
Deep Neural Network, Marigold, Multimodal image.
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