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NTIS 바로가기한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.40 no.5, 2022년, pp.449 - 456
최연오 (Lodics Co.,LTD) , 이재현 (Lodics Co.,LTD) , 심재후 (Lodics Co.,LTD) , 이승우 (Lodics Co.,LTD)
This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Net...
Choi, S.C. (2016), Crop Yields Estimation Using Spatial Panel Regression Model, Master's thesis, Chonnam National University, Gwangju, Korea, 33p. (in Korean with English abstract)
Kim, N., Ha, K.J., Park, N.W., Cho, J., Hong, S., and Lee, Y.W. (2019), A comparison between major artificial intelligence models for crop yield prediction: Case study of the midwestern united states, 2006-2015, ISPRS International Journal of Geo-Information, Vol. 8, 240. https://doi.org/10.3390/ijgi8050240
Kim, J.H. and Kim, K.D. (2015), An outlook on chinese cabbage production by cultivation type under the RCP8.5 projected climate, Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference-2015, 25 August, Jeonju, Korea, pp. 183-186. (in Korean with English abstract)
Kim, S.W. and Kim, Y.h. (2021), A study on the application of machine learning algorithm to predict crop production, Journal of the Korea Academia-Industrial cooperation Society, Vol. 22, No. 7, pp. 403-408. (in Korean with English abstract) https://doi.org/10.5762/KAIS.2021.22.7.403
Khaki, S., and Wang, L. (2019), Crop yield prediction using deep neural networks, Frontiers in Plant Science, Vol. 10, article 621. https://doi.org/10.3389/fpls.2019.00621
Khaki, S., Wang, L. and Archontoulis, S. V, (2020), A CNNRNN framework for crop yield prediction, Frontiers in Plant Science, Vol. 10, article 1750. https://doi.org/10.3389/fpls.2019.01750
Poggio L., de Sousa L.M., Batjes H.N., Heuvelink B.M.G., Kempen B., Ribeiro E., and Rossiter D., SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, Vol. 7, pp. 217-240, 2021 https://doi.org/10.5194/soil-7-217-2021
Lee, J.H., Lee, H.J., Kim, S.K., Lee, S.G., Lee, H.S., and Choi, C.S. (2017), Development of growth models as affected by cultivation season and transplanting date and estimation of prediction yield in kimch cabbage, Journal of BioEnvironment Control, Vol. 26, No. 4, pp. 235-241. (in Korean with English abstract) https://doi.org/10.12791/KSBEC.2017.26.4.235
Lee, J.G. and Moon, A. (2015), Yield forecasting method for smart farming, Proceedings of the Korean Institute of Information and Commucation Sciences Conference-2015. 26 October, Busan, Korea, pp. 619-622. (in Korean with English abstract)
National Honam Agricultural Experiment Station (2003), Soil Survey Theory and Practical Skills, National Honam Agricultural Experiment Station, National Institute of Agricultural Sciences, Jeollabuk-do Iksan (in Korean)
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