# Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
###### Abstract  
The presented study was aimed at investigating the variability for drought tolerance among potato cultivars. To achieve this, the stability of drought tolerance of potato cultivars under different water regime conditions was inspected during 11 years of consecutive experiments. The data on 50 potato cultivars' responses to drought stress, based on the morphological features of plants, i.e., leaf and stem mass and size of the assimilation area, have been collected. The tuber yield, as well as calculated plant tolerance indexes and Climatic Water Balance for each growing season, were analyzed. The studied cultivars were later assigned into one of three tolerance groups for soil drought. The highest linear relationship was found between the mass of leaves and stems and the tuber yield but was found too weak to raise any conclusions. Thus, the ensemble learning models have been evaluated and returned better performance results, and the final classifier is the implementation of extreme gradient boosting. The final classifier of the 96.7% accuracy, which used several measured potato parameters (Relative yield decrease, Stem mass, Maturity, Assimilation area, Leaves mass, Yield per plant, calculated Climatic water balance, and indices: MSTI and DSI) that could distinguish the different tolerance groups were evaluated in the study. potato; drought; drought stress tolerance; yield; machine learning +
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are grown at average day temperatures between 14 and 22 \({}^{\circ}\)C (287.15 and 295.15 \({}^{\circ}\)K) [8]. The susceptibility of potato crops to high temperatures largely depends on the genotype [9], development stage, and stress duration [10]; tuber initiation and bulking are the most critical stages [11; 12]. In potato plants, the minimum right temperature plays a crucial role during tuberization, which is reduced at the night temperatures above 20 \({}^{\circ}\)C (293.15 \({}^{\circ}\)K) with complete inhibition above 25 \({}^{\circ}\)C (298.15 \({}^{\circ}\)K). Potatoes are also sensitive to drought, mainly due to the crop's shallow root system and the low capacity of recuperation after a period of water stress [13]. Potatoes have a sparse and shallow root system [14] with a depth ranging from 0.5 to 1.0 m [15]. About 85% of the total root length is concentrated in the upper 0.3 m of soil [16]. Due to this, potatoes extract less of the available water from the soil compared to other crops [17]. Information about phenotyping under replete conditions can provide data that can be used to identify characteristics associated with improved performance under specific stress. The most frequently used morphological and physiological indicators of plants in response to drought stress are leaves mass, leaves assimilation area, the relative water content in leaves, and the SPAD index. Concerning yield, these are the yield mass in stress conditions; yield mass in control conditions; yield decrease; and drought indexes like the Drought Susceptible Index (DSI), Drought Tolerance Index (DTI), and MSTI Moderate Drought Tolerance Index (MSTI) [18]. The observation of different plants parameters measured and denoted in different ways (as continuous or categorical variables) and the attempt to merge this information requires the application of the methodology that allows one to model and evaluate the studied phenomena. Nowadays, the widespread use of the machine learning algorithms could be remarked on also in the field of agronomy [19]. The ensemble learning methods are worth notice especially, which allow one to get satisfying results even for sometimes noisy agronomic measurements, as they bring a way of combining weak classifiers into groups of better performances [20], even for datasets of limited sizes [21].