Simple comparison of averages
The easiest and most common is to display the mean and standard error for each year.
If you have more years, you might then want to add indication on the graph corresponding to p.values of pairwise comparisons but I don't think you have more than two years.
Below is a short R code that does the job. Note that I estimated the standard error with bootstrap. You might want to switch to use Agresti-Coull method. Personally, I like bootstrap. Make sure to use the standard errors that make sense given the statistical test you performed on these data.
# Function to calculate the standard error
bootstrap_SE = function(x,reps=1e5, SEorCI="SE")
means = vector("numeric",reps)
for (rep in 1:reps)
means[rep] = mean(sample(x,replace=TRUE))
if (SEorCI == "SE") return ( sqrt(var(means)) )
if (SEorCI == "CI") return ( quantile(means, c(0.025,0.975)) )
# Your data
d2015 = c(19.50, 7.12, 15.33, 12.00, 18.58, 23.83, 32.17, 17.00, 26.25, 16.92)
d2016 = c(22.92, 11.67, 19.67, 22.33, 20.92, 25.83, 25.67, 22.83, 28.42, 17.92)
# Calculate the averages
yAverages = c(mean(d2015),mean(d2016))
# Calculate standard errors
SE = c(bootstrap_SE(d2015),bootstrap_SE(d2016))
x = c(2015,2016)
# Make the plot
plot(y=yAverages,x=x, xlab="year", ylab="gland count", ylim=c(min(yAverages-SE),max(yAverages+SE)), xlim = c(2014.4,2016.5), xaxp=c(2015,2016,1))
Focus on systematic differences
If your goal is to insist on the existence of systematic differences for each plant, then I would recommend @kwah's solution (+1). I don't quite like @Shawn's solution (no offense), I found the graphs complicated, unintuitive for categorical variables and I found that they do a poor job to convey the concept of systematic differences in comparison to @kwah's solution.
If you're thinking at doing a paired-test, then going with @kwah's solution sounds like a clever idea.