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.
Esirige et al. (Sun,) studied this question.