Quantitative evaluation standards are often required to accurately predict sprouting regeneration, particularly for cutting quality. However, the coppicing surface targets cannot be recognized in complex fields during Caragana korshinskii shrub coppicing in the arid regions of Northwest China. In this study, a quantitative evaluation was proposed for sprouting regeneration, according to the synergistic association between cutting morphologies and agronomic traits. Accurate recognition of coppicing surfaces was achieved in unstructured field environments. A segmentation network was constructed, named WoodGrainNet. Firstly, the global context modelling and target localization were enhanced via a semantic stream using a lightweight Transformer architecture. Secondly, a frequency-domain stream was incorporated with the Haar discrete wavelet transform to effectively suppress abiotic background interference for the texture representation of the targets. Simultaneously, a shape stream was combined with a differentiable Sobel operator. Explicit constraints were also applied to high-frequency gradient regions. Thereby, the adjacent cut boundaries were delineated to effectively alleviate the adhesion of dense targets. Three-stream features were synergistically simulated for the deep integration. The WoodGrainNet significantly improved the segmentation robustness and instance discrimination of the improved model in complex scenes, particularly at the level of underlying physical feature representation. Accurate segmentation was achieved to automatically extract a key geometric phenotypic indicator for coppicing quality—coppicing surface circularity (C). Accordingly, the coppicing quality grading and sprouting prediction were established after evaluation. The experimental results demonstrate that the better performance of WoodGrainNet was achieved to balance high accuracy and real-time processing, with a mean Intersection over Union (mIoU) of 86.99% and an inference speed of 51.78 frames/s. The performance was significantly superior to mainstream networks, such as DeepLabV3+, effectively recognizing tiny coppicing targets. In terms of agronomic analysis, statistical results revealed that there was a significant positive correlation between the morphological quality of the coppicing surface and the sprouting potential of lateral branches. Notably, the coefficient of determination (R²) between the coppicing surface circularity (C) and the number of lateral branch sprouts reached 0.764 at the 5th week (N). The circularity served as an important morphological indicator to evaluate coppicing quality and sprouting regeneration. Field tests were conducted to verify its feasibility for engineering applications. The improved model was deployed on a mobile intelligent system. A discrimination accuracy of 90.8% was achieved for coppicing quality grades under mobile working conditions. Sprouting prediction trend was highly consistent with the measured ones, indicating its potential to replace manual inspection and subjective empirical judgment. The pixel-level phenotypic analysis of the coppicing surface was effectively transformed to predict the single-plant regeneration. The finding can provide a theoretical basis and technical support for the quality evaluation, parameter monitoring, and precise detection of ecological shrub forests in arid regions.