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http://dx.doi.org/10.7780/kjrs.2018.34.4.4

Artificial Neural Network-based Model for Predicting Moisture Content in Rice Using UAV Remote Sensing Data  

Sarkar, Tapash Kumar (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Ryu, Chan-Seok (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Kang, Jeong-Gyun (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Kang, Ye-Seong (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Jun, Sae-Rom (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Jang, Si-Hyeong (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Park, Jun-Woo (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Song, Hye-Young (Department of Bio-Systems Engineering, College of Agricultural and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science))
Publication Information
Korean Journal of Remote Sensing / v.34, no.4, 2018 , pp. 611-624 More about this Journal
Abstract
The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).
Keywords
ANN; Moisture content; Model simulation; Precision agriculture; UAV remote sensing;
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