Detection of moisture of flowing grain with a novel deep learning structure using 2D spectrogram data

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dc.authoridIşıker, Hakan/0000-0002-6465-8480
dc.authoridAksoy, Abdullah/0000-0002-2400-989X
dc.contributor.authorYiğit, Enes
dc.contributor.authorAksoy, Abdullah
dc.contributor.authorDuysak, Hüseyin
dc.contributor.authorIşıker, Hakan
dc.date.accessioned2025-01-12T17:19:41Z
dc.date.available2025-01-12T17:19:41Z
dc.date.issued2024
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractIn order to preserve the stored grain for a long time without deterioration, the moisture content must be accurately known. In this study, the moisture content of flowing grain was determined using radar spectrogram data and CNN structure. A free-space measurement technique-based experimental environment was established for the purpose of collecting the necessary signals to measure the moisture content. The measurement plant is composed of two horn antennas, a vector network analyzer (VNA), and a flowing grain mechanism. In the experimental environment, the S11 and S21 parameter values are recorded from the VNA. The short-time Fourier transforms (STFT) of the recorded signals were then taken, and 2D spectrogram images were created. The dataset, comprising 22,780 images, was split into 80 % for training and 20 % for testing; then, they were subjected to regression analysis using CNNs. First, the mean absolute error (MAE) values of the 27 pre-trained CNN architectures in a single epoch are calculated. Subsequently, the architecture with the best four regression results is automatically selected. The training and testing steps are performed independently for each selected architecture, and the results are recorded. The MAE, root mean square error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) metrics are employed to assess the efficacy of the CNN architectures. Among these, ResNet50 is the architecture that yields the most favorable results. Subsequently, a subsequent architecture with fewer parameters and a more expeditious processing time is proposed. The novel deep-learning CNN architecture demonstrated superior performance compared to the pre-trained architectures. The results are as follows: MAE = 0.0411, MSE = 0.0149, RMSE = 0.122, and MAPE = 0.0397. When comparing the time spent on training and testing, the least time-consuming architecture required approximately 72 min, whereas this study was completed in approximately 325 s. The pronounced disparity is readily apparent. The results demonstrate that when the CNN is appropriately modeled and trained, the combination of CNN and appropriate signal processing can effectively determine the moisture content of grains.
dc.identifier.citationYigit, E., Aksoy, A., Duysak, H., & Işiker, H. (2024). Detection of moisture of flowing grain with a novel deep learning structure using 2D spectrogram data. Computers and Electronics in Agriculture, 224. https://doi.org/10.1016/j.compag.2024.109180
dc.identifier.doi10.1016/j.compag.2024.109180
dc.identifier.issn0168-1699
dc.identifier.issn1872-7107
dc.identifier.scopus2-s2.0-85196314627
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compag.2024.109180
dc.identifier.urihttps://hdl.handle.net/11492/10161
dc.identifier.volume224
dc.identifier.wosWOS:001259160400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorDuysak, Hüseyin
dc.institutionauthoridDuysak, Hüseyin/0000-0002-2748-0660
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofComputers and Electronics in Agriculture
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectConvolutional neural network (CNN)
dc.subjectDeep learning (DL)
dc.subjectGrain moisture content
dc.subjectMoisture measurement
dc.titleDetection of moisture of flowing grain with a novel deep learning structure using 2D spectrogram data
dc.typeArticle

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