Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/522
Title: Determination of the Amount of Grain in Silos With Deep Learning Methods Based on Radar Spectrogram Data
Authors: Duysak, Hüseyin
Özkaya, Umut
Yiğit, Enes
Keywords: Convolutional Neural Network (Cnn)
Deep Learning
Grain Quantity Measurement
Radar-Level Measurement
Vision
Model
Issue Date: 2021
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract: Since the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0-20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.
URI: https://doi.org/10.1109/TIM.2021.3085939
https://hdl.handle.net/20.500.13091/522
ISSN: 0018-9456
1557-9662
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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