Low-cost plant phenotyping for individual plants is increasingly important for plant digital twins, breeding, and continuous crop monitoring. This review surveys representative studies on image-based plant phenotyping from 2015 to 2025 and organizes them into two major categories: deep-learning-based trait estimation from RGB/RGB-D images and low-cost 3D phenotyping based on multi-view reconstruction. The literatures indicate that deep learning methods are advantageous for rapid, automated, and high-throughput estimation of specific traits, but they often depend on annotated data and may show limited crossscenario generalization. In contrast, 3D reconstruction methods usually provide richer structural information and broader trait coverage, but they require more complex acquisition workflows and higher computational burden. Therefore, low-cost plant phenotyping should be understood not only in terms of sensor price, but also in terms of the overall trade-off among sensing, computation, labor, and deployability. Finally, this review summarizes current challenges and identifies future directions, including cost-aware benchmarking, lightweight image-based models, robust field 3D reconstruction, and tighter integration with plant digital twin systems.