Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JASTMO-17-2_002
تاریخ نمایه سازی: 1 آذر 1402
Abstract:
The goal of this study was to predict the moisture content of paddy using machine vision and artificial neural networks (ANNs). The grains were dried as thin layer with air temperatures of ۳۰, ۴۰, ۵۰, ۶۰, ۷۰, and ۸۰°C and air velocities of ۰.۵۴, ۱.۱۸, ۱.۵۶, ۲.۴۸ and ۳.۲۷ ms-۱. Kinetics of L*a*b* were measured. The air temperature, air velocity, and L*a*b* values were used as ANN inputs. The results showed that with increase in drying time, L* decreased, but a* and b* increased. The effect of air temperature and air velocity on the L*a*b* values were significant (P< ۰.۰۱) and not significant (P> ۰.۰۵), respectively. Changing of color values at ۸۰°C was more than other temperatures. The optimized ANN topology was found as ۵-۷-۱ with Logsig transfer function in hidden layer and Tansig in output layer. Mean square error, coefficient of determination, and mean absolute error of the optimized ANN were ۰.۰۰۱, ۰.۹۶۳۰, and ۰.۰۳۱, respectively.
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Authors
I. Golpour
Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
R. Amiri Chayjan
Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
J. Amiri Parian
Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
J. Khazaei
Department of Agricultural Technical Engineering, College of Abouraihan, University of Tehran, Islamic Republic of Iran.
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