CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf–air temperature difference

Abstract

Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf–air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type ‘Tainan ASVEG No. 19’, large fruits breeding line ‘108290’, and wild accession ‘LA2093’). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.

Exploring Efficient Methods for Using Multiple Spectral Reflectance Indices to Establish a Prediction Model for Early Drought Stress Detection in Greenhouse Tomato

“…Although irrigation refers to the atmospheric or soil conditions that improve plant growth and increase crop yield [16], conducting irrigation based on the plant response is sometimes more appropriate and accurate [17][18][19]. Initially, the plants respond to stress by reducing one or several physiological functions, and most plants activate their stresscoping mechanisms in this phase.…”