🤖 AI Summary
This study addresses the absence of high-quality, pixel-level segmentation datasets for sesame plants—a critical gap that has hindered the application of artificial intelligence in precision agriculture. To bridge this gap, we present the first publicly available, high-resolution image segmentation dataset specifically curated for sesame crops, comprising 292 images captured in Nigerian fields using smartphone cameras. Pixel-level annotations were generated using Segment Anything Model v2 under the supervision of local farmers and released in a YOLO-compatible format. Evaluation using YOLOv8 demonstrates the dataset’s efficacy, achieving mAP@0.5 of 84% for detection and mAP@0.5:0.95 of 58% and 52% for detection and segmentation tasks, respectively. This resource fills a significant void in visual data for sesame farming in Africa and supports future AI-driven agricultural innovations.
📝 Abstract
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.