Proximal near-infrared hyperspectral imaging dataset for identifying epicuticular wax loss in Masena blueberries to evaluate post-harvest quality

TL;DR

This paper introduces a dataset of hyperspectral images of blueberries to study wax loss and assess post-harvest quality using near-infrared imaging.

Contribution

The dataset provides labeled hyperspectral images for evaluating epicuticular wax loss and harvesting methods in blueberries.

Findings

Hyperspectral imaging captured differences in wax states and harvesting methods on blueberry surfaces.

The dataset includes 49 images across five categories for machine learning analysis.

The data enable research on post-harvest quality assessment using spectral analysis.

Abstract

The dataset presented in this paper consists of hyperspectral images of Masena blueberries that were harvested on November 24, 2023, from an orchard in Pukehina, New Zealand. Blueberries were hand-harvested with gloves (intact wax), hand-harvested (without gloves), and mechanically aided by picking via a handheld shaker. Some berries were also wiped to eliminate degrading epicuticular wax (EW) for comparison. Imaging was performed within 9 hours of harvest using a Specim FX17e hyperspectral camera (900–1700 nm, 224 bands) under controlled lighting conditions. The data were white and dark reference-normalized, annotated using the in-house HAPPy tool (ENVI Software), and saved in MATLAB (.mat) format for analysis. A total of 49 individual hyperspectral images were captured from 39 blueberry fruits to capture multiple views or surface states. We provide 5 spectral hypercube sets of data collected with the hyperspectral camera: ‘Assisted Harvested Blueberries (AHB)’ (10 images), ‘Hand Harvested Blueberries (HHB)’ (10 images), ‘Perfect EW’ (10 images), ‘No EW’ (9 images), and ‘No EW vs. Perfect EW‘ (10 images: 5 from ‘No EW‘ and 5 from ‘Perfect EW‘). This dataset, collected and archived by the University of Waikato (WaI2M: Waikato Instrumentation and Measurement Research Group, Hyperspectral Imaging Group), enables near-infrared hyperspectral imaging research in agriculture for EW classification and detection, harvesting method classification, and fruit surface property spectral analysis using machine/deep learning methods.

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