We present CaryaData, an image dataset of Chinese hickory (Carya cathayensis Sarg.) fruit maturity acquired in natural orchards in Zhejiang Province, China. The dataset comprises 1,661 canopy images (3024 × 3024 pixels) spanning key developmental stages from fruit enlargement to harvest. Within these images, 3,211 visually discernible fruit instances are annotated with axis-aligned bounding boxes and assigned to three maturity levels (maturity1–maturity3) or an unknown class for visually uncertain cases (labelled as unknown in the released annotations), based on pericarp colour, surface blemishes and cracking status. Data construction followed a rigorous quality-control workflow, including automated image quality filtering, standardised maturity interpretation, and a two-round annotation process with double-blind cross-validation of all images. To facilitate modelling and quantitative analysis, CaryaData also provides a derived subset uniformly resized to 640 × 640 pixels, together with image-level and instance-level metadata describing illumination, maturity composition and geometric properties of targets. Physical detachment force experiments confirmed the biological consistency of the maturity grading, and benchmark experiments showed that CaryaData supports deep learning-based fruit maturity assessment, offering a reusable resource for research on maturity evaluation, yield estimation and intelligent harvesting in Chinese hickory orchards.
Jia et al. (Fri,) studied this question.
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