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This paper presents an innovative method for reconstructing 3D models from 2D stereoscopic images captured from the front, back, left, and right sides of an object at 90 - degree horizontal rotations. The proposed process involves several key steps: plant class segmentation using artificial intelligence (AI), disparity and depth mapping from stereo images, point cloud generation, merging multiple point clouds into a single unified point cloud, and mesh application to ensure surface continuity of the 3D model. Recognizing the critical role of accurate segmentation in 3D reconstruction, this study compares two AI segmentation architectures-YOLOv8 and Detectron2-to determine which performs better in terms of segmentation accuracy, training speed, and memory consumption for the plant class. This research focuses on the 3D reconstruction phase of a parent study titled "Investigating Deep Learning for Predicting and Simulating Plant Growth Structures: A Preparatory Effort Towards the Digital Twin Paradigm in Agriculture," whose dataset comprises 2D stereo images of plants that require 3D visualization. Its long-term vision is to develop a user experience that assists farmers in making informed decisions by leveraging predictive models and 3D visualizations of crop growth.