{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:44:59Z","timestamp":1776613499302,"version":"3.51.2"},"reference-count":30,"publisher":"Fuji Technology Press Ltd.","issue":"2","funder":[{"name":"Osaka Electro-Communication University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRM","J. Robot. Mechatron."],"published-print":{"date-parts":[[2026,4,20]]},"abstract":"<jats:p>\n                    In this study, we addressed agricultural labor shortages by developing a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height (\n                    <jats:italic>PH<\/jats:italic>\n                    ), and leaf count (L\n                    <jats:sub>\n                      <jats:italic>n<\/jats:italic>\n                    <\/jats:sub>\n                    ). Multivariable regression confirmed that integrated solar radiation (\n                    <jats:italic>S<\/jats:italic>\n                    ) was the most dominant factor, although broccoli growth involved a complex interplay of solar radiation, optimal temperature, humidity, and soil moisture. More importantly, the analysis revealed that the middle layer soil moisture (u\n                    <jats:sub>\n                      <jats:italic>m<\/jats:italic>\n                    <\/jats:sub>\n                    ) exhibited the strongest positive contribution to\n                    <jats:italic>PH<\/jats:italic>\n                    . This finding indicated that water availability in the main root zone was essential for vertical growth and highlighted the indispensability of multipoint sensing over conventional single-depth measurements to accurately model the intricate relationship between soil moisture and crop development. Moving forward, we aim to leverage the superiority of multipoint data to construct a sophisticated growth prediction model, thereby contributing to the optimization of irrigation and temperature management in smart farming systems.\n                  <\/jats:p>","DOI":"10.20965\/jrm.2026.p0471","type":"journal-article","created":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:02:06Z","timestamp":1776610926000},"page":"471-482","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modeling of Crop Growth Using a Smart Agriculture Measurement Module Composed of Multipoint Soil Moisture Sensor and Environmental Sensors"],"prefix":"10.20965","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-8728","authenticated-orcid":true,"given":"Katsushi","family":"Ogawa","sequence":"first","affiliation":[{"name":"Osaka Electro-Communication University, 18-8 Hatsucho, Neyagawa-shi, Osaka 572-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wakana","family":"Ono","sequence":"additional","affiliation":[{"name":"Osaka Electro-Communication University, 18-8 Hatsucho, Neyagawa-shi, Osaka 572-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seonghee","family":"Jeong","sequence":"additional","affiliation":[{"name":"Osaka Electro-Communication University, 18-8 Hatsucho, Neyagawa-shi, Osaka 572-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"key-10.20965\/jrm.2026.p0471-1","unstructured":"United Nations, Department of Economic and Social Affairs, \u201cPopulation Division,\u201d 2019."},{"key":"key-10.20965\/jrm.2026.p0471-2","doi-asserted-by":"crossref","unstructured":"E. Play\u00e1n and L. Mateos, \u201cModernization and optimization of irrigation systems to increase water productivity,\u201d Agricultural Water Management, Vol.80, pp. 100-116, 2006. https:\/\/doi.org\/10.1016\/j.agwat.2005.07.007","DOI":"10.1016\/j.agwat.2005.07.007"},{"key":"key-10.20965\/jrm.2026.p0471-3","doi-asserted-by":"crossref","unstructured":"M. H. Ali and M. S. U Talukder, \u201cIncreasing water productivity in crop production \u2013 A synthesis,\u201d Agricultural Water Management, Vol.95, pp. 1201-1213, 2008. https:\/\/doi.org\/10.1016\/j.agwat.2008.06.008","DOI":"10.1016\/j.agwat.2008.06.008"},{"key":"key-10.20965\/jrm.2026.p0471-4","doi-asserted-by":"crossref","unstructured":"P. Debaeke and A. Aboudrare, \u201cAdaptation of crop management to water-limited environments,\u201d European J. of Agronomy, Vol.21, No.4, pp. 433-446, 2004. https:\/\/doi.org\/10.1016\/j.eja.2004.07.006","DOI":"10.1016\/j.eja.2004.07.006"},{"key":"key-10.20965\/jrm.2026.p0471-5","doi-asserted-by":"crossref","unstructured":"Z. Ahmed, D. Gui, G. Murtaza, L. Yunfei, and S. Ali, \u201cAn Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands,\u201d Agronomy, Vol.13, Article No.2113, 2023. https:\/\/doi.org\/10.3390\/agronomy13082113","DOI":"10.3390\/agronomy13082113"},{"key":"key-10.20965\/jrm.2026.p0471-6","doi-asserted-by":"crossref","unstructured":"I. Tornese, A. Matera, M. Rashvand, and F. Genovese, \u201cUse of Probes and Sensors in Agriculture-Current Trends and Future Prospects on Intelligent Monitoring of Soil Moisture and Nutrients,\u201d AgriEngineering, Vol.6, No.4, pp. 4154-4181, 2024. https:\/\/doi.org\/10.3390\/agriengineering6040234","DOI":"10.3390\/agriengineering6040234"},{"key":"key-10.20965\/jrm.2026.p0471-7","doi-asserted-by":"crossref","unstructured":"I. Lephondo, A. Telukdarie, I. Munien, U. Onkonkwo, and A. Vermeulen, \u201cThe Outcomes of Smart Irrigation System using Machine Learning to minimize water usage within the Agriculture Sector,\u201d Procedia Computer Science, Vol.237, pp. 525-532, 2024. https:\/\/doi.org\/10.1016\/j.procs.2024.05.136","DOI":"10.1016\/j.procs.2024.05.136"},{"key":"key-10.20965\/jrm.2026.p0471-8","doi-asserted-by":"crossref","unstructured":"H. M. A. E. Baki, H. Fujimaki, I. Tokumoto, and T. Saito, \u201cOptimization of irrigation scheduling using crop-water simulation, water pricing, and quantitative weather forecasts,\u201d Frontiers in Agronomy, Vol.6, Article No.1376231, 2024. https:\/\/doi.org\/10.3389\/fagro.2024.1376231","DOI":"10.3389\/fagro.2024.1376231"},{"key":"key-10.20965\/jrm.2026.p0471-9","doi-asserted-by":"crossref","unstructured":"B. Nsoh, A. Katimbo, H. Guo, D. M. Heeren, H. N. Nakabuye, X. Qiao, Y. Ge, D. R. Rudnick, J. Wanyama, E. Bwambale, and S. Kiraga, \u201cInternet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review,\u201d Sensors, Vol.24, No.23, Article No.7480, 2024. https:\/\/doi.org\/10.3390\/s24237480","DOI":"10.3390\/s24237480"},{"key":"key-10.20965\/jrm.2026.p0471-10","doi-asserted-by":"crossref","unstructured":"Y. Zhao, G. Li, S. Li, Y. Luo, and Y. Bai, \u201cA Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation-Optimization Model,\u201d Water, Vol.16, No.17, Article No.2545, 2024. https:\/\/doi.org\/10.3390\/w16172545","DOI":"10.3390\/w16172545"},{"key":"key-10.20965\/jrm.2026.p0471-11","doi-asserted-by":"crossref","unstructured":"M. Ohishi, M. Takahashi, M. Fukuda, and F. Sato, \u201cDeveloping a Growth Model to Predict Dry Matter Production in Broccoli (Brassica oleracea L. var. italica) \u2018Ohayou\u2019,\u201d The Horticulture J., Vol.92, No.1, pp. 77-87, 2023. https:\/\/doi.org\/10.2503\/hortj.QH-022","DOI":"10.2503\/hortj.QH-022"},{"key":"key-10.20965\/jrm.2026.p0471-12","unstructured":"S. Yamazaki, Y. Kiriiwa, and M. Aono, \u201cEstimation Evaluation of Strawberry Harvest Based on Regression Analysis with Integrated and Different Values,\u201d The Institute of Electronics, Information and Communication Engineers,\u201d Vol.J101-D, No.10, pp. 1466-1470, 2018."},{"key":"key-10.20965\/jrm.2026.p0471-13","doi-asserted-by":"crossref","unstructured":"B. Petrovi\u0107, R. Bumb\u00e1lek, T. Zoubek, R. Kune\u0161, L. Smutn\u00fd, and P. Barto\u0161, \u201cApplication of precision agriculture technologies in Central Europe-review,\u201d J. of Agriculture and Food Research, Vol.15, Article No.101048, 2024. https:\/\/doi.org\/10.1016\/j.jafr.2024.101048","DOI":"10.1016\/j.jafr.2024.101048"},{"key":"key-10.20965\/jrm.2026.p0471-14","doi-asserted-by":"crossref","unstructured":"M. Raj and M. Prahadeeswaran, \u201cRevolutionizing agriculture: a review of smart farming technologies for a sustainable future,\u201d Discover Applied Sciences, Vol.7, Article No.937, 2025. https:\/\/doi.org\/10.1007\/s42452-025-07561-6","DOI":"10.1007\/s42452-025-07561-6"},{"key":"key-10.20965\/jrm.2026.p0471-15","doi-asserted-by":"crossref","unstructured":"C. J. Bryant, G. D. Spencer, D. M. Gholson, M. T. Plumblee, D. M. Dodds, G. R. Oakley, D. Z. Reynolds, and L. J. Krutz, \u201cDevelopment of a soil moisture sensor-based irrigation scheduling program for the Midsouthern United States,\u201d Crop Forage Turfgrass Management, Vol.9, No.1, Article No.e20217, 2023. https:\/\/doi.org\/10.1002\/cft2.20217","DOI":"10.1002\/cft2.20217"},{"key":"key-10.20965\/jrm.2026.p0471-16","doi-asserted-by":"crossref","unstructured":"V. Kumar, K. V. Sharma, N. Kedam, A. Patel, T. R. Kate, and U. Rathnayake, \u201cA comprehensive review on smart and sustainable agriculture using IoT technologies,\u201d Smart Agricultural Technology, Vol.8, Article No.100487, 2024. https:\/\/doi.org\/10.1016\/j.atech.2024.100487","DOI":"10.1016\/j.atech.2024.100487"},{"key":"key-10.20965\/jrm.2026.p0471-17","doi-asserted-by":"crossref","unstructured":"T. Okayama, \u201cRecommendation for Promoting Smart Agriculture Using Low-Cost Open Source Hardware,\u201d Japanese J. of Farm Work Research, Vol.55, No.3, pp. 169-172, 2020. https:\/\/doi.org\/10.4035\/jsfwr.55.169","DOI":"10.4035\/jsfwr.55.169"},{"key":"key-10.20965\/jrm.2026.p0471-18","doi-asserted-by":"crossref","unstructured":"J. van de Gevel, J. van Etten, and S. Deterding, \u201cCitizen science breathes new life into participatory agricultural research. A review,\u201d Agronomy for Sustainable Development, Vol.40, Article No.35, 2020. https:\/\/doi.org\/10.1007\/s13593-020-00636-1","DOI":"10.1007\/s13593-020-00636-1"},{"key":"key-10.20965\/jrm.2026.p0471-19","doi-asserted-by":"crossref","unstructured":"A. Z. Bayih, J. Morales, Y. Assabie, and R. A. de By, \u201cUtilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture,\u201d Sensors, Vol.22, No.9, Article No.3273, 2022. https:\/\/doi.org\/10.3390\/s22093273","DOI":"10.3390\/s22093273"},{"key":"key-10.20965\/jrm.2026.p0471-20","doi-asserted-by":"crossref","unstructured":"C. Corbari, N. Paciolla, I. B. Charfi, D. Skokovic, J. A. Sobrino, and M. Woods, \u201cCitizen science supporting agricultural monitoring with hundreds of low-cost sensors in comparison to remote sensing data,\u201d European J. of Remote Sensing, Vol.55, No.1, pp. 388-408, 2022. https:\/\/doi.org\/10.1080\/22797254.2022.2084643","DOI":"10.1080\/22797254.2022.2084643"},{"key":"key-10.20965\/jrm.2026.p0471-21","unstructured":"P. Taechatanasat and L. Armstrong, \u201cDecision Support System Data for Farmer Decision Making,\u201d Proc. of Asian Federation for Information Technology in Agriculture, pp. 472-486, 2014."},{"key":"key-10.20965\/jrm.2026.p0471-22","doi-asserted-by":"crossref","unstructured":"D. C. Rose and T. J. A. Bruce, \u201cFinding the right connection: what makes a successful decision support system?,\u201d Food Energy Secur., Vol.7, No.1, 2017. https:\/\/doi.org\/10.1002\/fes3.123","DOI":"10.1002\/fes3.123"},{"key":"key-10.20965\/jrm.2026.p0471-23","doi-asserted-by":"crossref","unstructured":"K. Momii, J. Nozaka, and T. Yano, \u201cComparison of root water uptake models,\u201d J. of Japan Society of Hydrology and Water Resources, Vol.5, No.3, pp. 13-21, 1992. https:\/\/doi.org\/10.3178\/jjshwr.5.3_13","DOI":"10.3178\/jjshwr.5.3_13"},{"key":"key-10.20965\/jrm.2026.p0471-24","doi-asserted-by":"crossref","unstructured":"D. M. El-Shikha, P. Waller, D. Hunsaker, T. Clarke, and E. Barnes, \u201cGround-based remote sensing for assessing water and nitrogen status of broccoli,\u201d Agricultural Water Management, Vol.92, No.3, pp. 183-193, 2007. https:\/\/doi.org\/10.1016\/j.agwat.2007.05.020","DOI":"10.1016\/j.agwat.2007.05.020"},{"key":"key-10.20965\/jrm.2026.p0471-25","unstructured":"S. Yamazaki, Y. Kiriiwa, Nonmember, and M. Aono, \u201cEstimation Evaluation of Strawberry Harvest Based on Regression Analysis with Integrated and Different Values,\u201d IEICE Trans. on Information and Systems, Vol.J101-D, No.10, pp. 1466-1470, 2018."},{"key":"key-10.20965\/jrm.2026.p0471-26","doi-asserted-by":"crossref","unstructured":"J. Fan, B. McConkey, H. Wang, and H. Janzen, \u201cRoot distribution by depth for temperate agricultural crops,\u201d Field Crops Research, Vol.189, pp. 68-74, 2016. https:\/\/doi.org\/10.1016\/j.fcr.2016.02.013","DOI":"10.1016\/j.fcr.2016.02.013"},{"key":"key-10.20965\/jrm.2026.p0471-27","doi-asserted-by":"crossref","unstructured":"Y. M\u00fcllers, J. A. Postma, H. Poorter, J. Kochs, D. Pflugfelder, U. Schurr, and D. van Dusschoten, \u201cShallow roots of different crops have greater water uptake rates per unit length than deep roots in well\u2011watered soil,\u201d Plant and Soil, Vol.481, pp. 475-493, 2022. https:\/\/doi.org\/10.1007\/s11104-022-05650-8","DOI":"10.1007\/s11104-022-05650-8"},{"key":"key-10.20965\/jrm.2026.p0471-28","doi-asserted-by":"crossref","unstructured":"G.-R. Yu, J. Zhuang, K. Nakayama, and Y. Jin, \u201cRoot water uptake and profile soil water as affected by vertical root distribution,\u201d Plant Ecology, Vol.189, No.1, pp. 15-30, 2007. https:\/\/doi.org\/10.1007\/s11258-006-9163-y","DOI":"10.1007\/s11258-006-9163-y"},{"key":"key-10.20965\/jrm.2026.p0471-29","doi-asserted-by":"crossref","unstructured":"P. Gherbin, V. Miccolis, and V. Candido, \u201cRoot length density and yield traits of broccoli (Brassica oleracea L. var. italica Plenck) as affected by different techniques of seedling growing and transplanting,\u201d Acta Horticulturae, Vol.1005, No.1005, pp. 427-434, 2013. https:\/\/doi.org\/10.17660\/ActaHortic.2013.1005.51","DOI":"10.17660\/ActaHortic.2013.1005.51"},{"key":"key-10.20965\/jrm.2026.p0471-30","doi-asserted-by":"crossref","unstructured":"N. Li, T. H. Skaggs, P. Ellegaard, A. Bernal, and E. Scudiero, \u201cRelationships among soil moisture at various depths under diverse climate, land cover and soil texture,\u201d Science of the Total Environment, Vol.947, Article No.174583, 2024. https:\/\/doi.org\/10.1016\/j.scitotenv.2024.174583","DOI":"10.1016\/j.scitotenv.2024.174583"}],"container-title":["Journal of Robotics and Mechatronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=robot003800020011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:02:54Z","timestamp":1776610974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jrm\/rb\/robot003800020471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4,20]]},"published-print":{"date-parts":[[2026,4,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jrm.2026.p0471","relation":{},"ISSN":["1883-8049","0915-3942"],"issn-type":[{"value":"1883-8049","type":"electronic"},{"value":"0915-3942","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,20]]}}}