Determination of Citrullus Lanatus "Sweet-16" Ripeness Using Android-Based Application

Watermelon is one of the most mouth-watering fruits that people like to eat, especially when it comes to summer-a nondestructive way of determining the ripeness of watermelon considered as a challenge for its customers. This study addresses the problem of identifying between ripe and unripe watermel...

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Veröffentlicht in:2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) S. 1 - 6
Hauptverfasser: Villa, Anthony B., Jacinto, Rogie P., Ramos, Michell Ann A., Alagao, Stephen Paul L.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.06.2021
Schlagworte:
Android Mobile Application
Buildings
Convolutional neural networks
Convolutional Neural Networks (CNN)
Image processing
Neural networks
Open CV-Python
Training
Watermelon Ripeness
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Zusammenfassung:Watermelon is one of the most mouth-watering fruits that people like to eat, especially when it comes to summer-a nondestructive way of determining the ripeness of watermelon considered as a challenge for its customers. This study addresses the problem of identifying between ripe and unripe watermelon using an android mobile to be available remotely. The application of a scientific strategy for determining ripeness is through image processing, which is a more capable, non-destructive, and cost-effective method. Classified samples of Sweet-16 watermelon from the farm and wet market were processed using Open-CV Python and running Tensorflow as the backend for Keras for building and training the CNN classifier. Classification of Sweet-16 watermelon is Unripe and Ripe, and Unknown. The study achieved an overall accuracy of 89.52% regardless of the position of the watermelon as captured.
DOI:10.1109/ICECCE52056.2021.9514216