  Cold temperatures during fall and spring have the potential to cause frost
damage to grapevines and other fruit plants, which can significantly decrease
harvest yields. To help prevent these losses, farmers deploy expensive frost
mitigation measures such as sprinklers, heaters, and wind machines when they
judge that damage may occur. This judgment, however, is challenging because the
cold hardiness of plants changes throughout the dormancy period and it is
difficult to directly measure. This has led scientists to develop cold
hardiness prediction models that can be tuned to different grape cultivars
based on laborious field measurement data. In this paper, we study whether deep
learning models can improve cold hardiness prediction for grapes based on data
that has been collected over a 30-year time period. A key challenge is that the
amount of data per cultivar is highly variable, with some cultivars having only
a small amount. For this purpose, we investigate the use of multi-task learning
to leverage data across cultivars in order to improve prediction performance
for individual cultivars. We evaluate a number of multi-task learning
approaches and show that the highest performing approach is able to
significantly improve over learning for single cultivars and outperforms the
current state-of-the-art scientific model for most cultivars.
