  In the era of large sky surveys, photometric redshifts (photo-z) represent
crucial information for galaxy evolution and cosmology studies. In this work,
we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with
neural Networks (GaZNet-1), which uses both images and multi-band photometry
measurements to predict galaxy redshifts, with accuracy, precision and outlier
fraction superior to standard methods based on photometry only. As a first
application of this tool, we estimate photo-z of a sample of galaxies in the
Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on $\sim140 000$
galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic
redshifts are available from different surveys. This sample is dominated by
bright (MAG$\_$AUTO$<21$) and low redshift ($z < 0.8$) systems, however, we
could use $\sim$ 6500 galaxies in the range $0.8 < z < 3$ to effectively extend
the training to higher redshift. The inputs are the r-band galaxy images plus
the 9-band magnitudes and colours, from the combined catalogs of optical
photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree
Infrared survey. By combining the images and catalogs, GaZNet-1 can achieve
extremely high precision in normalized median absolute deviation (NMAD=0.014
for lower redshift and NMAD=0.041 for higher redshift galaxies) and low
fraction of outliers ($0.4$\% for lower and $1.27$\% for higher redshift
galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also
shows a $\sim 10-35$% improvement in precision at different redshifts and a
$\sim$ 45% reduction in the fraction of outliers. We finally discuss that, by
correctly separating galaxies from stars and active galactic nuclei, the
overall photo-z outlier fraction of galaxies can be cut down to $0.3$\%.
