This work presents quantitative detection of water stress and estimation of the water stress
level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of
the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional
neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection
and estimation of water stress in potato crops is carried out on two different phenological stages
of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms
are trained with a small subset of each hyperspectral image corresponding to the plant canopy. The
results are improved using majority voting to classify all the canopy pixels in the hyperspectral
images. The results indicate that both detection of water stress and estimation of the level of water
stress can be obtained with good accuracy, improved further by majority voting. The importance of
each band of the hyperspectral images in the classification of the images is assessed by random forest
and extreme gradient boost, which are the machine learning algorithms that perform best overall on
both phenological stages and detection and estimation of water stress in potato crops.