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Three kinds of sensors are combined to estimate the water content in soil: a resistivity sensor composed of two brass electrodes, a commercial air humidity sensor interfaced with the soil by a filter membrane of PTFE with polyester scrim, and an RGB sensor used for visible reflectance spectroscopy. We show their integration and embeddability in a burrowing growing robot based on additive manufacturing with a 4\u00a0cm probe diameter. The multimodal sensing strategy has been characterized and tested in clay and sand medium at different water content. Results show that the resistive sensor works in all the tested ranges but is prone to failure due to electrode-soil contact issues. The air humidity sensor works accurately in a range of water content less than 5% (dry conditions), and the RGB sensor works in the 5\u201320% range. We propose a statistical approach for soil moisture estimation that combines all three technologies and demonstrate that we can accurately predict the water content in our experimental soils, clay and sand, with better performance in clay (Root Mean Square Error, RMSE\u2009=\u20090.38). The proposed miniaturized multimodal sensing strategy can enable long-term, in-situ soil moisture monitoring functionalities in self-deployable robots for precision agriculture and forestry applications.<\/jats:p>","DOI":"10.1007\/978-3-031-38857-6_15","type":"book-chapter","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T07:02:35Z","timestamp":1690786955000},"page":"184-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Miniature Soil Moisture Sensors for a Root-Inspired Burrowing Growing Robot"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6874-1970","authenticated-orcid":false,"given":"Emanuela","family":"Del Dottore","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2303-2844","authenticated-orcid":false,"given":"Alessio","family":"Mondini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7286-2042","authenticated-orcid":false,"given":"Davide","family":"Bray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0722-8350","authenticated-orcid":false,"given":"Barbara","family":"Mazzolai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","volume":"114","author":"DJ Mulla","year":"2013","unstructured":"Mulla, D.J.: Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. 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