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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Vydáno v:	2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) s. 1 - 6
Hlavní autoři:	Villa, Anthony B., Jacinto, Rogie P., Ramos, Michell Ann A., Alagao, Stephen Paul L.
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 12.06.2021
Témata:	Android Mobile Application Buildings Convolutional neural networks Convolutional Neural Networks (CNN) Image processing Neural networks Open CV-Python Training Watermelon Ripeness
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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