Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. If you use the dataset, please cite the following articles:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting, Data in Brief, vol. 26, p. 104514, Oct. 2019.[2] H. Altaheri, M. Alsulaiman, and G. Muhammad, Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning, IEEE Access, vol. 7, pp. 117115-117133, 2019. Updates: Dataset-2 was uploaded. Date: 16/05/2019. The pdf files of dataset documentation and experiment papers were attached. Date: 23/10/2019. New zip file, named DATASET-1 (224 X 224) Categorized.zip, was uploaded. This file contains the images of dataset-1 that resized to 224x224 pixels and categorized into subfolders according to date type, maturity, and the harvesting decision. This categorization, and image resolution, is related to the experiments described in ref [2]. Date: 09/02/2020.