In this particular case, accuracy/precision and other sensible considerations don't matter. If the information that you gave in the question is all you know, then you have to sell bananas in region 1. Period.
However, if the price of bananas in region 2 was higher than in region 1, it would have been a different story. The reason is that the price asymmetry would bring the cost of error asymmetry.
What if in reality the region 2 liked bananas more than region 1? In this case although given the data it seems that region 1 has higher propensity to buy bananas, depending on the price differential the cost of error could be too high given the variances of your estimates.
@whuber brought up an interesting topic: risk aversion. If you're averse to risks, then you may consider expected utility theory, it's studied in microeconomics and game theory. The trouble is that there's not enough data to set up the expected utility function.
For instance, in finance the simple examples are usually like follows. You have two stocks. Stock 1 returns 80% annually with standard deviation 60%, Stock 2 returns 50% annually with standard deviation 5%. Which stock to choose?
You need a utility function, such as $U(\mu,\sigma)=\mu-\sigma^2$. In this case $U_1=44%$ and $U_2=0.4975$., so you have to go with the Stock 2 despite its average return us lower, because it's risk is "too high".