  Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar
population and brown dwarfs, from M7 V to cooler objects with L, T, and Y
spectral types. Most of them have been discovered using wide-field imaging
surveys, for which the Virtual Observatory (VO) has proven to be of great
utility. We aim to perform a search for UCDs in the entire Javalambre
Photometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$)
following a VO methodology. We also explore the ability to reproduce this
search with a purely machine learning (ML)-based methodology that relies solely
on J-PLUS photometry. We followed three different approaches based on
parallaxes, proper motions, and colours, respectively, using the VOSA tool to
estimate the effective temperatures. For the ML methodology, we built a
two-step method based on principal component analysis and support vector
machine algorithms. We identified a total of 7827 new candidate UCDs, which
represents an increase of about 135% in the number of UCDs reported in the sky
coverage of the J-PLUS second data release. Among the candidate UCDs, we found
122 possible unresolved binary systems, 78 wide multiple systems, and 48
objects with a high Bayesian probability of belonging to a young association.
We also identified four objects with strong excess in the filter corresponding
to the Ca II H and K emission lines and four other objects with excess emission
in the H$\alpha$ filter. With the ML approach, we obtained a recall score of
92% and 91% in the test and blind test, respectively. We consolidated the
proposed search methodology for UCDs, which will be used in deeper and larger
upcoming surveys such as J-PAS and Euclid. We concluded that the ML methodology
is more efficient in the sense that it allows for a larger number of true
negatives to be discarded prior to analysis with VOSA, although it is more
photometrically restrictive.
