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Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellarpopulation and brown dwarfs, from M7 V to cooler objects with L, T, and Yspectral types. Most of them have been discovered using wide-field imagingsurveys, for which the Virtual Observatory (VO) has proven to be of greatutility. We aim to perform a search for UCDs in the entire JavalambrePhotometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$)following a VO methodology. We also explore the ability to reproduce thissearch with a purely machine learning (ML)-based methodology that relies solelyon J-PLUS photometry. We followed three different approaches based onparallaxes, proper motions, and colours, respectively, using the VOSA tool toestimate the effective temperatures. For the ML methodology, we built atwo-step method based on principal component analysis and support vectormachine algorithms. We identified a total of 7827 new candidate UCDs, whichrepresents an increase of about 135% in the number of UCDs reported in the skycoverage of the J-PLUS second data release. Among the candidate UCDs, we found122 possible unresolved binary systems, 78 wide multiple systems, and 48objects with a high Bayesian probability of belonging to a young association.We also identified four objects with strong excess in the filter correspondingto the Ca II H and K emission lines and four other objects with excess emissionin the H$\alpha$ filter. With the ML approach, we obtained a recall score of92% and 91% in the test and blind test, respectively. We consolidated theproposed search methodology for UCDs, which will be used in deeper and largerupcoming surveys such as J-PAS and Euclid. We concluded that the ML methodologyis more efficient in the sense that it allows for a larger number of truenegatives to be discarded prior to analysis with VOSA, although it is morephotometrically restrictive.

J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology