Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset
This provides a novel dataset for sesame plant segmentation in Nigeria, supporting applications like plant monitoring and yield estimation, but it is incremental as it applies existing methods to new data.
This paper tackles the problem of developing AI models for agricultural applications by creating the Sesame Plant Segmentation Dataset, an open-source annotated image dataset with 206 training, 43 validation, and 43 test images in YOLO format, and evaluation using YOLOv8 showed strong performance, such as 84% mean average precision at IoU 0.50 for detection and segmentation.
This paper presents the Sesame Plant Segmentation Dataset, an open source annotated image dataset designed to support the development of artificial intelligence models for agricultural applications, with a specific focus on sesame plants. The dataset comprises 206 training images, 43 validation images, and 43 test images in YOLO compatible segmentation format, capturing sesame plants at early growth stages under varying environmental conditions. Data were collected using a high resolution mobile camera from farms in Jirdede, Daura Local Government Area, Katsina State, Nigeria, and annotated using the Segment Anything Model version 2 with farmer supervision. Unlike conventional bounding box datasets, this dataset employs pixel level segmentation to enable more precise detection and analysis of sesame plants in real world farm settings. Model evaluation using the Ultralytics YOLOv8 framework demonstrated strong performance for both detection and segmentation tasks. For bounding box detection, the model achieved a recall of 79 percent, precision of 79 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 58 percent. For segmentation, it achieved a recall of 82 percent, precision of 77 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 52 percent. The dataset represents a novel contribution to sesame focused agricultural vision datasets in Nigeria and supports applications such as plant monitoring, yield estimation, and agricultural research.