{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:02:18Z","timestamp":1775934138085,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Environment of Korea as The SS (Surface Soil conservation and management) projects","award":["2019002820001"],"award-info":[{"award-number":["2019002820001"]}]},{"name":"Ministry of Environment of Korea as The SS (Surface Soil conservation and management) projects","award":["PJ01476804"],"award-info":[{"award-number":["PJ01476804"]}]},{"name":"Cooperative Research Program for Agriculture Science &amp; Technology Development","award":["2019002820001"],"award-info":[{"award-number":["2019002820001"]}]},{"name":"Cooperative Research Program for Agriculture Science &amp; Technology Development","award":["PJ01476804"],"award-info":[{"award-number":["PJ01476804"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R2) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R2 of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R2 of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R2 of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.<\/jats:p>","DOI":"10.3390\/s23041976","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:09:59Z","timestamp":1675994999000},"page":"1976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3528-6630","authenticated-orcid":false,"given":"Soo-Hwan","family":"Park","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Smart Agriculure, Kangwon National University, Chuncheon 24341, Republic of Korea"}]},{"given":"Bo-Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Smart Agriculure, Kangwon National University, Chuncheon 24341, Republic of Korea"}]},{"given":"Min-Jee","family":"Kim","sequence":"additional","affiliation":[{"name":"Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon 24341, Republic of Korea"}]},{"given":"Wangyu","family":"Sang","sequence":"additional","affiliation":[{"name":"Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration, Hyoksin-ro 181, Iseo-myeon, Wanju-gun 55365, Republic of Korea"}]},{"given":"Myung Chul","family":"Seo","sequence":"additional","affiliation":[{"name":"Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration, Hyoksin-ro 181, Iseo-myeon, Wanju-gun 55365, Republic of Korea"}]},{"given":"Jae-Kyeong","family":"Baek","sequence":"additional","affiliation":[{"name":"Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration, Hyoksin-ro 181, Iseo-myeon, Wanju-gun 55365, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8641-6442","authenticated-orcid":false,"given":"Jae E","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9088-5978","authenticated-orcid":false,"given":"Changyeun","family":"Mo","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Smart Agriculure, Kangwon National University, Chuncheon 24341, Republic of Korea"},{"name":"Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon 24341, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s12571-010-0108-x","article-title":"Crops that feed the World 2. Soybean\u2014Worldwide production, use, and constraints caused by pathogens and pests","volume":"3","author":"Hartman","year":"2011","journal-title":"Food Secur."},{"key":"ref_2","unstructured":"Ashley, D.A. (1983). Crop-Water Relations, Wiley."},{"key":"ref_3","first-page":"246","article-title":"Agricultural drought: Indices, definition and analysis","volume":"Volume 286","author":"Rodda","year":"2004","journal-title":"The Basis of Civilization\u2013Water Science?"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2016.02.064","article-title":"Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index","volume":"177","author":"Gumuzzio","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Feki, M., Ravazzani, G., Ceppi, A., Milleo, G., and Mancini, M. (2018). Impact of Infiltration Process Modeling on Soil Water Content Simulations for Irrigation Management. Water, 10.","DOI":"10.3390\/w10070850"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1007\/s42853-021-00118-6","article-title":"IoT for Promoting Agriculture 4.0: A Review from the Perspective of Weather Monitoring, Yield Prediction, Security of WSN Protocols, and Hardware Cost Analysis","volume":"46","author":"Majumdar","year":"2021","journal-title":"J. Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahmad, N., Malagoli, M., Wirtz, M., and Hell, R. (2016). Drought stress in maize causes differential acclimation responses of glutathione and sulfur metabolism in leaves and roots. BMC Plant Biol., 16.","DOI":"10.1186\/s12870-016-0940-z"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agrformet.2007.05.004","article-title":"Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts","volume":"146","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/JSTARS.2016.2639338","article-title":"Intercomparison of soil moisture, evaporative stress, and vegetation indices for estimating corn and soybean yields over the US","volume":"10","author":"Mladenova","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1007\/s42853-022-00158-6","article-title":"Simulation of Soil Water Movement in Upland Soils Under Sprinkler and Spray Hose Irrigation Using HYDRUS-1D","volume":"47","author":"Kim","year":"2022","journal-title":"J. Biosyst. Eng."},{"key":"ref_11","unstructured":"Park, S.W. (1996, January 1). Simulating potential crop yields and probable damages from abnormal weather conditions. Proceedings of the Korea Water Resources Association Conference, Seoul, Republic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"433","DOI":"10.3741\/JKWRA.2009.42.6.433","article-title":"The Stochastic Behavior of Soil Water and the Impact of Climate Change on Soil Water","volume":"42","author":"Han","year":"2009","journal-title":"J. Korea Water Resour. Assoc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s42853-021-00117-7","article-title":"A review on the effect of soil compaction and its management for sus-tainable crop production","volume":"46","author":"Shaheb","year":"2021","journal-title":"J. Biosyst. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.3390\/s100403314","article-title":"A FDR Sensor for Measuring Complex Soil Dielectric Permittivity in the 10\u2013500 MHz Frequency Range","volume":"10","author":"Skierucha","year":"2010","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Veres, M., Lacey, G., and Taylor, G.W. (2015, January 3\u20135). Deep Learning Architectures for Soil Property Prediction. Proceedings of the 2015 12th Conference on Computer and Robot Vision, Halifax, NS, Canada.","DOI":"10.1109\/CRV.2015.15"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3788\/fgxb20173801.0109","article-title":"Soil near-infrared spectroscopy prediction model based on deep sparse learning","volume":"38","author":"Wang","year":"2017","journal-title":"Chin. J. Lumin."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, D., Liu, C., Tang, Y., and Gong, C. (2022). A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection. Sustainability, 14.","DOI":"10.3390\/su14031386"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.2166\/wst.2020.369","article-title":"A comprehensive review of deep learning applications in hydrology and water resources","volume":"82","author":"Sit","year":"2020","journal-title":"Water Sci. Technol."},{"key":"ref_20","first-page":"9","article-title":"Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM","volume":"15","author":"Shin","year":"2017","journal-title":"J. Korean Inst. Inf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, Y., and Ren, X. (2020). Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network. Algorithms, 13.","DOI":"10.3390\/a13070173"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.1002\/hyp.13540","article-title":"Using data-driven methods to explore the predict-ability of surface soil moisture with FLUXNET site data","volume":"33","author":"Pan","year":"2019","journal-title":"Hydrol. Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.agwat.2010.08.021","article-title":"Distribution of roots and root length density in a maize\/soybean strip intercropping system","volume":"98","author":"Gao","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_26","first-page":"157","article-title":"Effect of organic resources application on crop yield and soil physical preperties of upland","volume":"17","author":"Han","year":"2017","journal-title":"Korean J. Soil Sci. Fertil."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, P., Qiu, H., Lan, Y., Wang, W., Chen, W., Han, X., and Lu, J. (2022). Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory. Agriculture, 12.","DOI":"10.3390\/agriculture12010025"},{"key":"ref_28","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_29","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Lauderdale, FL, USA."},{"key":"ref_30","first-page":"30","article-title":"Regional soil moisture prediction system based on Long Short-Term Memory network","volume":"213","author":"Brdar","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1111\/j.1752-1688.2006.tb04512.x","article-title":"Soil moisture prediction using support vector machines","volume":"42","author":"Gill","year":"2006","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hong, Z., Kalbarczyk, Z., and Iyer, R.K. (2016, January 18\u201320). A data-driven approach to soil moisture collection and prediction. Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, USA.","DOI":"10.1109\/SMARTCOMP.2016.7501673"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199097","DOI":"10.1109\/ACCESS.2020.3034984","article-title":"A Deep Learning Approach for Multi-Depth Soil Water Content Prediction in Summer Maize Growth Period","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1071\/SR12228","article-title":"Monitoring and prediction of soil moisture spatial\u2013temporal variations from a hydropedological perspective: A review","volume":"50","author":"Zhu","year":"2012","journal-title":"Soil Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.proeng.2017.02.475","article-title":"A Data Mining System for Real Time Soil Moisture Prediction","volume":"181","author":"Matei","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_36","unstructured":"Mathew, A., Amudha, P., and Sivakumari, S. (2021). Advanced Machine Learning Technologies and Applications, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"700","DOI":"10.2136\/sssaj1963.03615995002700060038x","article-title":"Influence of straw mulch on soil moisture, soil temperature and the growth of corn","volume":"27","author":"Moody","year":"1963","journal-title":"Soil Sci. Soc. Am. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:29:47Z","timestamp":1760120987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041976"],"URL":"https:\/\/doi.org\/10.3390\/s23041976","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,10]]}}}