Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1075
Title: Developing a deep neural network model for predicting carrots volume
Authors: Örnek, Mustafa Nevzat
Örnek, Humar Kahramanlı
Keywords: Carrots physical properties
Deep neural network
Deep feedforward networks
Stochastic gradient descent
Recurrent neural networks
Long short-term memory
Issue Date: 2021
Publisher: SPRINGER
Abstract: In this paper, a deep learning approach to predict carrots volume according to the physical properties was designed. A total of 464 carrots were used for volume prediction. The used carrots were taken from Kasinhani, Konya. First, the data was produced. For this, the length, the diameters with 5 cm intervals, and the volume of each carrot were measured and recorded. The measurements were done using a steel ruler, a vernier caliper, and a glass graduated cylinder. Two deep learning methods: DFN and LSTM were developed to predict carrot volume. The developed systems were implemented with the Keras library for Python. Statistical measures such as Root Mean Squared Error, Mean Absolute Error, and R-2 were used to determine the predicting accuracy of the system. Both methods produced very close values. DFN and LSTM networks achieved 0.9765 and 0.9766 R-2, respectively. RMSE values were 0.0312 for both models. The results obtained showed that both DFN and LSTM are successful and applicable to this task.
URI: https://doi.org/10.1007/s11694-021-00923-9
https://hdl.handle.net/20.500.13091/1075
ISSN: 2193-4126
2193-4134
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
Teknik Bilimler Meslek Yüksekokulu Koleskiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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