Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management.

Unmanned aerial systems (UASs) and artificial intelligence (AI) allow for the effective monitoring of the plants, but it is difficult to determine the stages of cotton development in the process of irrigation gradients. In this paper, UAS images were combined with deep learning to conduct field-scale cotton phenology classification in graded drought situations. SegNet, U-Net, and DeepLabv3+ were trained on various sample sizes and tested on global accuracy (GA), mean intersection-over-union (mIoU), and mean boundary F-score (mBF). It was found that DeepLabv3+ outperformed all other methods and yielded the most uniform delineation of crop row spacing, canopy edges, and boll opening boundaries throughout the entire growing season. Under single-stage training, performance became stable at training sample sizes ≥ 960 for the seedling and squaring stages, whereas the boll and boll-opening stages required ≥ 1280; for full-season training, performance became stable when the sample size reached 4480 (GA = 0.98, mIoU = 0.95, mBF = 0.81). Cross-treatment evaluation indicated that errors were mainly concentrated between adjacent stages, with higher confusion under the 0% irrigation treatment and more stable identification results under the 90% irrigation treatment. A DAP 138 field survey (36 points) confirmed an irrigation-gradient phenological shift from boll-opening dominance at 0% irrigation to universal boll at 90% irrigation, consistent with spatial phenology maps. Overall, the proposed framework provides a cost-effective, field-scale solution to support precision irrigation management in arid cotton-growing regions.