Research Information System
3rd Cognitive Models and Artificial Intelligence Conference, AICCONF 2025, Prague, Czech Republic, 13 - 14 June 2025, (Full Text)
The sunn pest poses a significant threat to agriculture, causing damage to some of the most vital cultivars. The pest penetrates and damages grains, leaving an enzyme that renders them unsuitable for bakery goods production. Therefore, detecting damaged wheat grains for separation is crucial in flour production facilities to ensure uninterrupted production. For the solution to such problems, we present a new dataset of wheat grains that focuses on sunn pest damage to six different wheat cultivars, namely Bezostaja, Müfitbey, Nacibey, Sönmez-2001, Tosunbey, and Ekiz, which are the cultivars that were made in Türkiye. The dataset consists of 83 sunn-pest-damaged and 87 healthy wheat images. In addition, there are 170 images containing 3565 wheat grains. Our dataset differs from the others due to its various cultivars and the condition regarding whether it is healthy or sunn-pest-damaged. This unique feature makes our dataset particularly suitable for developments such as sunn pest damage detection and grain segmentation. Additionally, because the dataset consists of different cultivars, it can be used for classification. Given that the wheat grains are presented in bulk and exhibit different orientations and shape factors, our dataset encourages developers to grapple with and devise solutions for real-life problems.Our data available at https://doi.org/10.17632/gmw48bvxdz.1