Abstract: Poplar tree is one of the most important tree species in wood production and protective forest construction. The poplar artificial forest cultivation is ranked first in the world. Current severe drought and typical abiotic stress under global warming can pose a great challenge to the production of wood raw materials and the protection of forest resources. A series of planting strategies (such as the identification of water-deficient plants) have been used to cultivate the poplar species of high drought tolerance against drought stress. These approaches are required for the rapid observation and analysis of the growth status of poplar saplings, in order to execute irrigation decisions or drought-resistant variety screening. Previous research has focused mainly on the detection of drought stress, where the macroscopic morphology of plants can be classified to predict the drought level. Only a few studies have been focused on the digital expression of water deficiency symptoms in plants. Alternatively, the water content of plants has been directly predicted using relatively complex and expensive sensing devices. In this article, a low-cost machine vision was adopted to realize the lant phenotyping of poplar saplings under drought stress. Firstly, an extraction framework of the poplar skeleton was proposed to identify the individual leaves and key nodes inside the plant using the YOLOv8-pose detection model, in order to extract the overall morphological and structural information of the plant. Secondly, the few-shot learning techniques were introduced, including the training dataset expansion (or considered as an automatic synthesis for datasets) using minimal manual annotation. The annotated leaves and stems were extracted from the images, and then added into a ‘component pool’. New images of the poplar plant were also selected randomly. All the individual leaves and the key points were detected (including the leaf tip, connection point between blade and petiole, and connection point between the petiole and the trunk). Among them, the few-shot learning was trained using 600 synthetic poplar images with only 10 manually annotated samples. Thirdly, the angles of the main vein and petiole in each leaf were calculated from the skeleton information of the poplar sapling. Furthermore, the frequency distribution vector was converted (ranges from -90° to 90°, with an interval of 10°) for the artificial intelligence-based modeling. Finally, partial least square discrimination analysis (PLSDA), support vector machine (SVM), multilayer perception (MLP), and convolutional neural network (CNN) classifiers were compared to evaluate the drought stress level. The optimal model was selected for the final drought-stress grading. The results showed that the YOLOv8-pose model with the few-shot learning performed the best in the leaf recognition and key point extraction tasks, where the mean average precision with 0.5 as the threshold for intersection over union (mAP50) values were 0.798 and 0.914, respectively. The effectiveness and reliability few-shot learning-based YOLOv8-pose model were also validated using an independent test dataset composed of real samples and accurate manual annotations. Meanwhile, the manual annotation cost was significantly reduced for YOLOv8-pose model training. Subsequently, the classification model with one-dimensional CNN and leaf angle frequency distribution was superior in the drought stress grading task, with the highest classification accuracy of 0.850. In addition, the improved models shared relatively small parameters (model size less than 7 MB) with high speed. Low-cost computing can be realized in cloud service platforms with limited sources or embedded systems (such as Nvidia Jetson series devices). The phenotyping can also provide new technical support to identify the water-deficient poplar saplings and screen the drought-resistant plants.