Replication for: ForestSAM: A Novel Integration of DeepForest and SAM2 for Oil Palm Crown Segmentation in Aerial Imagery.

The oil palm industry in Colombia faces significant challenges in large-scale plantation management, particularly in the early detection of diseases that cause morphological changes in the crown of palm trees, severely impacting crop health and productivity. Early identification of these symptoms is typically performed manually through structural examination of the tree crowns, a process that is time-consuming, labor-intensive, and prone to human error. Although recent studies have made progress using deep learning techniques for tree counting, detection, and crown classification, the application of these methods for detailed crown segmentation remains largely unexplored. In this study, we propose ForestSAM, the first integration of DeepForest for oil palm tree detection with Segment Anything Model 2 (SAM2) for precise crown segmentation using drone and aerial imagery. The proposed method achieves a Dice coefficient (DSC) of 0.826 and a Jaccard index (IoU) of 0.712, showing its effectiveness in accurately segmenting oil palm crowns and laying the basis for timely disease detection, carbon capture metrics, and optimized resource allocation.