where $y$ is indexed by $i$ for the $i$-th subject (ID) and by $j$ for the $j$-th plot. The random intercepts for ID $u_{i}$ and for plot $v_{j}$ are colored in red.
You can report that equation as stated, and add that the coefficient of dd varies from individual to individual with a standard deviation of .215. And also that the individuals' intercepts vary with an SD of .523, and that the SD of error not accounted for by individuals is 2.613. This information is in the summary table of random effects.
If you are using treatment contrasts for your categorical variable (the default in R and as implied by your lmer output), the intercept represents the value of the response variable at the reference level (Level1) of the categorical variable and at a value of 0 for the continuous variable. As you have written the equation there are then 2 different coefficients hiding in what you have written as $\beta_1$, representing separately the difference of each of Level2 and Level3 from Level1 (again, at a value of 0 for the continuous variable). For $\beta_2$, the value reported is for cases when the categorical variable is at Level1. Thus there are 2 different coefficients hiding in what you have written as $\beta_3$, representing how much to add or subtract from $\beta_2$ when the categorical variable is at either Level2 or Level3 instead.
Implied in your equation is that the random effect is only involved in the intercept; note that you can also include random slopes.
There are other ways to report such results, but you have to specify other contrasts in your call to the regression function.
Best Answer
I think it could be written like this:
$$ y_{ij} = \beta_{0} + \beta_{1}X_{1ij} + \beta_{2}X_{2ij} + \beta_{3}X_{3ij} + \beta_{4}X_{2ij}X_{3ij} + \color{red}{u_{i}} + \color{red}{v_{j}} + \varepsilon_{ij} $$
where $y$ is indexed by $i$ for the $i$-th subject (ID) and by $j$ for the $j$-th plot. The random intercepts for ID $u_{i}$ and for plot $v_{j}$ are colored in red.