In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology.

Abstract

Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessment, which affects harvesting decisions. To solve this problem, this study utilized hyperspectral technology to collect data from 22 fruit trees in an orchard (in the range of 400-1000 nm) and explored the effectiveness of five regions of interest selection methods (x-axis, y-axis, four-quadrant, threshold segmentation, and raw) for the delineation of the citrus ripening stage. The data quality was enhanced via wavelet transform (WT)-multiple scattering correction (MSC) preprocessing, and the effective wavelengths were extracted via the successive projections algorithm (SPA). On the basis of these wavelengths, backpropagation neural network (BP) and convolutional neural network (CNN) models were built for maturity prediction. The results show that the x-axis region of interest selection method outperforms the other methods, and the SPA-BP model based on this method performs best. An accuracy of 99.19% for the correction set and 100% for the prediction set was achieved when only 0.03% of the wavelength was used. This groundbreaking study highlights the significant potential of hyperspectral technology for in situ assessment of citrus ripening stages. Furthermore, it offers crucial technical support and serves as a valuable reference for the advancement of precision agriculture.

Plant phenotyping relevance

柑橘果実の成熟段階という植物器官の状態を、ハイパースペクトル画像、ROI選択、前処理、波長選択、機械学習により非破壊推定する方法が研究の中心である。

Code and data availability

The supplied article blocks describe hyperspectral imaging of 'Shiranui' mandarin for ripeness classification, but contain no data availability statement, no public repository deposit, no author code/model release, and no supplement reference with datasets or trained models. Only the license URL and DOI appear; no code

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