  Prediction of stress conditions is important for monitoring plant growth
stages, disease detection, and assessment of crop yields. Multi-modal data,
acquired from a variety of sensors, offers diverse perspectives and is expected
to benefit the prediction process. We present several methods and strategies
for abiotic stress prediction in banana plantlets, on a dataset acquired during
a two and a half weeks period, of plantlets subject to four separate water and
fertilizer treatments. The dataset consists of RGB and thermal images, taken
once daily of each plant. Results are encouraging, in the sense that neural
networks exhibit high prediction rates (over $90\%$ amongst four classes), in
cases where there are hardly any noticeable features distinguishing the
treatments, much higher than field experts can supply.
