Abstract
Dragon fruit, often referred to as pitaya, is a tropical fruit with various types, including both white-fleshed and red-fleshed varieties. Its distinctive appearance is complemented by a range of potential health advantages. These include its abundance of nutrients and antioxidants, which contribute to a robust immune system, aid in blood sugar regulation, and support the well-being of the heart, bones, and skin. Consequently, the global desire for dragon fruit is yielding substantial economic advantages for developing nations like Bangladesh, which in turn underscores the pressing need for an automated system to identify the optimal harvest time and differentiate between fresh and defective fruits to ensure quality. To accomplish this objective, this paper introduces an extensive collection of high-resolution dragon fruits because effective detection by machine learning models necessitates a substantial amount of data. The dataset was painstakingly gathered during a span of four months from three distinct locations in Bangladesh, with the valuable assistance of domain experts. Possible application of the dataset encompasses quality evaluation, robotic harvesting, and packaging systems, ultimately boosting the effectiveness of dragon fruit production procedures. The dataset has the potential to be a valuable resource for researchers interested in dragon fruit cultivation, offering a solid foundation for the application of computer vision and deep learning methods in the agricultural industry.