{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:01:31Z","timestamp":1778407291721,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Centre for Research and Development of Poland","award":["No. POIR.04.01.04-00-0009\/19"],"award-info":[{"award-number":["No. POIR.04.01.04-00-0009\/19"]}]},{"name":"National Centre for Research and Development of Poland","award":["No. 59:4-3-00-3-02"],"award-info":[{"award-number":["No. 59:4-3-00-3-02"]}]},{"name":"Ministry of Agriculture, Poland (MRiRW)","award":["No. POIR.04.01.04-00-0009\/19"],"award-info":[{"award-number":["No. POIR.04.01.04-00-0009\/19"]}]},{"name":"Ministry of Agriculture, Poland (MRiRW)","award":["No. 59:4-3-00-3-02"],"award-info":[{"award-number":["No. 59:4-3-00-3-02"]}]},{"name":"Potato Agronomy Department, Plant Breeding and Acclimatization Institute-NRI, Division Jadwisin, Poland","award":["No. POIR.04.01.04-00-0009\/19"],"award-info":[{"award-number":["No. POIR.04.01.04-00-0009\/19"]}]},{"name":"Potato Agronomy Department, Plant Breeding and Acclimatization Institute-NRI, Division Jadwisin, Poland","award":["No. 59:4-3-00-3-02"],"award-info":[{"award-number":["No. 59:4-3-00-3-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work aimed to assess the recalibration and accurate characterization of commonly used smart soil-moisture sensors using computational methods. The paper describes an ensemble learning algorithm that boosts the performance of potato root moisture estimation and increases the simple moisture sensors\u2019 performance. It was prepared using several month-long everyday actual outdoor data and validated on the separated part of that dataset. To obtain conclusive results, two different potato varieties were grown on 24 separate plots on two distinct soil profiles and, besides natural precipitation, several different watering strategies were applied, and the experiment was monitored during the whole season. The acquisitions on every plot were performed using simple moisture sensors and were supplemented with reference manual gravimetric measurements and meteorological data. Next, a group of machine learning algorithms was tested to extract the information from this measurements dataset. The study showed the possibility of decreasing the median moisture estimation error from 2.035% for the baseline model to 0.808%, which was achieved using the Extra Trees algorithm.<\/jats:p>","DOI":"10.3390\/s22124591","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"4591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-1778","authenticated-orcid":false,"given":"Bogdan","family":"Ruszczak","sequence":"first","affiliation":[{"name":"Department of Computer Science, Opole University of Technology, 45-758 Opole, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2815-7507","authenticated-orcid":false,"given":"Dominika","family":"Boguszewska-Ma\u0144kowska","sequence":"additional","affiliation":[{"name":"Plant Breeding and Acclimatization Institute\u2014National Research Institute, Potato Agronomy Department, 05-870 Radzik\u00f3w, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Morales, F., Anc\u00edn, M., Fakhet, D., Gonz\u00e1lez-Torralba, J., G\u00e1mez, A.L., Seminario, A., Soba, D., Ben Mariem, S., Garriga, M., and Aranjuelo, I. 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