Scopus
YÖKSİS Eşleşti
Developing a deep neural network model for predicting carrots volume
Journal of Food Measurement and Characterization · Ağustos 2021
YÖKSİS Kayıtları
Developing a deep neural network model for predicting carrots volume
Journal of Food Measurement and Characterization · 2021 SCI-Expanded
PROFESÖR HUMAR KAHRAMANLI ÖRNEK →
Makale Bilgileri
DergiJournal of Food Measurement and Characterization
Yayın TarihiAğustos 2021
Cilt / Sayfa15 · 3471-3479
Scopus ID2-s2.0-85105191193
Özet
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 Kaşınhanı, 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 R2 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 R2, 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.
Yazarlar (2)
1
M. N. Ornek
2
Humar Kahramanli
Anahtar Kelimeler
Carrots physical properties
Deep feedforward networks
Deep neural network
Long short-term memory
Recurrent neural networks
Stochastic gradient descent
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
11
Atıf
2
Yazar
6
Anahtar Kelime