Segmenting plant root systems is vital in high-throughput phenotyping, providing insights into morphology, growth patterns, and stress responses. However, generating dense pixel-wise annotations for training segmentation models is labor-intensive and often impractical, especially for large datasets. To address this challenge, we propose a weakly supervised framework for root segmentation that relies only on sparse point-level annotations. Our model is based on a UNet architecture with a ResNet encoder and incorporates a composite loss function that combines binary cross-entropy, Dice loss, and consistency loss. This enables the model to learn accurate root structures even under limited supervision. We evaluate the proposed method on the ChronoRoot dataset and compare it with standard segmentation baselines. Despite relying on limited annotations, our method achieves competitive or superior performance to fully supervised and topology-aware models in Dice and precision scores. Through detailed ablation studies, we also demonstrate the individual contributions of each loss component to the overall performance. The proposed approach reduces annotation efforts while maintaining high segmentation accuracy, making it suitable for large-scale plant phenotyping applications.