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)) |
1 | Jiang, D., X.Yang, N. Clinton, and N. Wang, 2010. An artificial neural network model for estimating crop yields using remotely sensed information, International Journal of Remote Sensing, 25(9): 1723-1732. DOI |
2 | Kalisperakis, I.,C. Stentoumis, L. Grammatikopoulos, and K. Karantzalos, 2015. Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models, Proc. of International Conference on Aerial Vehiclesin Geomatics,Toronto,Canada, Aug. 30-Sep. 2, vol. XL-1/X4, pp. 299-303. |
3 | Kawamura, S., M. Natsuga, K. Takekura, and K. Itoh, 2003. Development of an automatic rice-quality inspection system, Computers and Electronics in Agriculture, 40(1-3): 115-126. DOI |
4 | Kunze, O. R. and S. Prasad, 1978. Grain fissuring potentials in harvesting and drying of rice, Transactions of the American Society of Agricultural Engineers, 21(2): 0361-0366. DOI |
5 | Lewis, C. D., 1982. International and Business Forecasting Methods: a practical guide to exponential smoothing and curve fitting, Butterworths Scientific Ltd., London, UK. |
6 | Lu, R., T. J. Siebenmorgen, T. A. Costello, and E. O. Fryar Jr., 1995. Effect of rice moisture content at harvest on economic return, Applied Engineering in Agriculture, 11(5): 685-690. DOI |
7 | Lu, R., T. J. Siebenmorgen, R. H. Dilday, and T. A. Costello, 1992. Modeling long-grain rice milling quality and yield during the harvest season, Transactions of the American Society of Agricultural Engineers, 35(6): 1905-1913. DOI |
8 | Manjunath, K. R., M. B. Potdar, and N. L. Purohit, 2002. Large area operational wheat yieldmodel development and validation based on spectral and meteorological data, International Journal of Remote Sensing, 23(15): 3023-3038. DOI |
9 | Volkers, K. C., M. Wachendorf, R. Loges, N. J. Jovanovic, and T. Taube, 2003. Prediction of the quality of forage maize by near-infrared reflectance spectroscopy, Animal Feed Science and Technology, 109(1-4): 183-194. DOI |
10 | Zarco-Tejada, P. J., V. Gonzalez-Dugo, and J. A. J. Berni, 2012. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sensing of Environment, 117: 322-337. DOI |
11 | Bautista,R.C.,T. Siebenmorgen, and P.Counce, 2007. Rice kernel dimensional variability trends, Transactions of the American Society of Agricultural and Biological Engineers, 23(2): 207-217. |
12 | Verma, L. R., 1994. New methodsfor on-the-farm rice drying: solar and biomass, In: Wayne, E. M. and Wadsworth, J. I. (Eds.), Rice Science and Technology, Marcel Dekker, Inc., New York, USA. |
13 | Nalley, L., B. Dixon, J. Tack, A. Barkley, and K. Jagadish, 2016. Optimal harvest moisture content for maximizing mid-south rice milling yields and returns, Agronomy Journal, 108(2): 701-712. DOI |
14 | Nash, J. E. and J. V. Sutcliffe, 1970. River flow forecasting through conceptual models: Part 1. Adiscussion of principles, Journal of Hydrology, 10(3): 282-290. DOI |
15 | Onoyama, H., C. Ryu, M. Suguri, and M. Iida, 2013. Potential of hyperspectral imaging for constructing a year-invariant model to estimate the nitrogen content ofrice plants at the panicle initiation stage, Proc. of International Federation of Automatic Control, Espoo, Finland, Aug. 27-30, vol. 46, pp. 219-224. |
16 | Akhand, K., M. Nizamuddin, and L. Roytman, 2018. AnArtificial Neural Network-Based Modelfor PredictingBoroRiceYield inBangladeshUsing AVHRR-Based Satellite Data, International Journal ofAgriculture and Forestry, 8(1): 16-25. |
17 | Asaka, D. and H. Shiga, 2003. Estimating rice grain protein contents with SPOT/HRVdata acquired atmaturing stage, Journal oftheRemote Sensing Society of Japan, 23(5): 451-457 (in Japanese with English abstract). |
18 | Banu, S., 2015. Precision agriculture: Tomorrow's technology for todays's Farmer, Journal of Food Processing and Technology, 6(8): 468. |
19 | Beale, M., M. Hagan, and H. Demuth, 1992. Neural Network ToolboxTM User's Guide, The MathWorks, Inc., Natick, MA, USA. |
20 | Bendig, J., A. Bolten, S. Bennertz, J. Broscheit, S. Eichfuss, and G. Bareth, 2014. Estimating biomass of barley using crop surface models (CSMs) derived fromUAV-basedRGBimaging, Remote Sensing, 6(11): 10395-10412. DOI |
21 | Ryu, C., M. Suguri, M. Iida, and M. Umeda, 2007. Validation of rice taste elements influenced by amount of nitrogen fertilizer and estimation using remote sensing, Journal of the Japanese Society of Agricultural Machinery, 69(1): 52-58 (in Japanese with English abstract). |
22 | Paswan, R. P. and S.A. Begum, 2013. Regression and neural networks models for prediction of crop production, International Journal of Scientific & Engineering Research, 4(9): 98-108. |
23 | Pettersson, C. G., M. Soderstrom, and H. Eckersten, 2006. Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors for within-field variability in grain yield and grain protein of malting barley, Precision Agriculture, 7(5): 343-359. DOI |
24 | Prasad, A. K., L. Chai, R. P. Singh, and M. Kafatos, 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters, International Journal of Applied Earth Observation and Geoinformation, 8(1): 26-33. DOI |
25 | Salazar, L., F. Kogan, and L. Roytman, 2007. Use of remote sensing data for estimation of winter wheat yield in the United States, International Journal of Remote Sensing, 28(17): 3795-3811. DOI |
26 | Santhi, C., J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan, and L. M. Hauck, 2001. Validation of the SWAT model on a large river basin with point and nonpointsources, Journal of American Water Resources Association, 37(5): 1169-1188. DOI |
27 | Saravanan, S., S. Kannan, and C. Thangaraj, 2012. Forecasting India's electricity demand using artificial neural network, Proc. of International conference on Advances in Engineering, Science and Managament, Nagapattinam,Tamil Nadu, India, Mar. 30-31, pp. 79-83. |
28 | Chen, C. and H. Mcnairn, 2007. A neural network integrated approach for rice crop monitoring, International Journal ofRemote Sensing, 27(7): 1367-1393. DOI |
29 | Sarkar, T. K.,C. Ryu, Y. Kang, S. Kim, S. Jeon, S. Jang, J. Park, S. Kim, and H. Kim, 2018. Integrating UAV Remote Sensing with GIS for Predicting Rice Grain Protein, Journal of Biosystems Engineering, 43(2): 148-159. DOI |
30 | Berni, J. A. J., P. J. Zarco-Tejada, L. Suarez, and E. Fereres, 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle, IEEE Transactions on Geoscience and Remote Sensing, 47(3): 722-738. DOI |
31 | Cnossen, A. G. and T. J. Siebenmorgen, 2000. The glass transition temperature concept in rice drying and tempering: Effect on milling quality, Transactions of the American Society of Agricultural Engineers, 43(6): 1661-1667. DOI |
32 | Food and Agriculture Organization of United Nations (FAO), 2016. Statistical database, Food and Agriculture Organization ofthe United Nations, http://faostat3.fao.org/home/E, Accessed onAug. 24, 2016. |
33 | Hansen, P. M., J. R. Jorgensen, and A. Thomsen, 2002. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression, Journal of Agriculture Science, 139(3): 307-318. DOI |
34 | Hunt, E. R., W. Dean Hively, S. J. Fujikawa, D. S. Linden, C. S. T. Daughtry, and G.W. McCarty, 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring, Remote Sensing, 2(1): 290-305. DOI |
35 | Abdulkadir, S. J., S. M. Shamsuddin, and R. Sallehuddin, 2012. Moisture Prediction in Maize using Three Term Back Propagation Neural Network, International Journal of Environmental Science and Development, 3(2): 199-204. |
36 | Siebenmorgen, T. J. and V. Jindal, 1986. Effects of moisture adsorption on the head rice yields of long-grain rice, Transactions of the American Society of Agricultural Engineers, 29(6): 1767-1771. DOI |
37 | Van Liew, M.W.,J. Arnold, and J. D. Garbrecht, 2003. Hydrologic simulation on agricultural watersheds: Choosing between two models, Transactions of the American Society of Agricultural and Biological Engineers, 46(6): 1539-1551. DOI |
38 | Siebenmorgen, T. J. and G. Qin, 2005. Relating rice kernel breaking force distributions to milling quality, Transactions of the American Society of Agricultural Engineers, 48(1): 223-228. DOI |
39 | Siebenmorgen, T. J., R. Bautista, and J. F. Meullenet, 2006. Predicting rice physicochemical properties using thickness fraction properties, Cereal Chemistry, 83(3): 275-283. DOI |
40 | Siebenmorgen, T. J., P. A. Counce, R. Lu, and M. F. Kocher, 1992. Correlation of head rice yield with individual kernel moisture content distribution at harvest, Transactions of the American Society of Agricultural Engineers, 35(6): 1879-1884. DOI |
41 | Stafford, J.V., 2000. Implementing precision agriculture in the 21st century, Journal of Agricultural Engineering Research, 76(3): 267-275. DOI |
42 | Thakur, A. K. and A. K. Gupta, 2006. Two stage drying of high moisture paddy with intervening rest period, Journal of Energy Conversion and Management, 47(18-19): 3069-3083. DOI |
43 | Islam, A. K. and S. K. Bala, 2008. Assessment of potato phenological characteristics using MODIS-derived NDVI and LAI Information, GIScience and Remote Sensing, 45(4): 454-470. DOI |
44 | Ismael, M.J., R.Ibrahim, and I. Ismail, 2011. Adaptive neural network prediction model for energy consumption, Proc. of 2011 3rd ICCRD International conference, Shanghai, China, Mar. 11-13, pp. 109-113. |
45 | Islam, M.R., N. Shimizu, and T. Kimura, 2004. Energy requirement in parboiling and its relationship to some important quality indicators, Journal of Food Engineering, 63(4): 433-439. DOI |