In order to group winter rapeseed cultivars according to evaluated traits, an experiment was conducted in the Research Greenhouse of Agriculture Faculty, University of Tabriz - IRAN. In the experiment were included 12 cultivars of winter rapeseed and 3 levels of water deficit stress. Gypsum blocks were used to monitor soil moisture. Water deficit stress was imposed from stem elongation to physiological maturity. According to the principal component analysis, five principal components were chosen with greater eigenvalue (more than 0.7) that are including 81.34% of the primeval variance of variables. The first component that explained the 48.02% of total variance had the high eigenvalue. The second component could justify about 13.64% of total variance and had positive association with leaf water potential and proline content and had negative relationship with leaf stomatal conductivity. The third, fourth and fifth components expressed around, 10.18, 4.83 and 4.68% of the total variance respectively. The third component had the high eigenvalue for plant dry weight. The fourth component put 1000-seed weight, seed yield, Silique per Plant and root dry weight against plant dry weight, chlorophyll fluorescence and leaf water potential. The fifth component had the high eigenvalue for root dry weight, root volume and 1000-seed weight.
Agricultural Stress Monitoring using Remote Sensing Data
