Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350–450 and 600–750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function–leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non–destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.

To identify seedling traits closely associated with drought resistance and to screen for drought-tolerant germplasm, 202 cotton varieties (lines) were evaluated under controlled indoor conditions using a nutrient soil cultivation method. Seedling-stage traits measured included plant height, cotyledon node diameter, true leaf number, chlorophyll content, and fresh and dry biomass of both shoots and roots. Drought resistance was assessed using drought resistance coefficients for each trait, followed by descriptive statistics, principal component analysis (PCA), partial correlation analysis, and comprehensive evaluation via the entropy weight method. PCA and partial correlation analysis revealed that plant height, cotyledon node diameter, aboveground fresh weight, and underground fresh weight were strongly associated with drought resistance at the seedling stage. The comprehensive drought resistance index (D-value) classified the 202 cotton lines into four categories: highly drought-resistant, moderately drought-resistant, drought-sensitive, and highly drought-sensitive. Physiological assays indicated that malondialdehyde (MDA) content in drought-resistant lines first increased and then declined with prolonged drought stress, while it continued to increase in sensitive lines. In contrast, proline (Pro) content and superoxide dismutase (SOD) activity increased steadily in drought-resistant lines but showed negligible changes in sensitive lines. These four morphological traits and three physiological indicators represent reliable criteria for evaluating drought resistance in cotton seedlings. Four highly drought-resistant and thirteen moderately drought-resistant lines were identified, providing valuable germplasm for genetic improvement of drought tolerance in cotton.