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基于高光譜成像的肥城桃品質(zhì)可視化分析與成熟度檢測
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國家自然科學(xué)基金項(xiàng)目(31701325、31671632)


Visual Detection of SSC and Firmness and Maturity Prediction for Feicheng Peach by Using Hyperspectral Imaging
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    摘要:

    肥城桃采摘后轉(zhuǎn)色快,、易腐爛,,導(dǎo)致果品等級下降。采用高光譜成像技術(shù)對其進(jìn)行可溶性固形物含量(SSC)和硬度可視化分析與成熟度檢測,,以提高果品質(zhì)量,,實(shí)現(xiàn)優(yōu)果優(yōu)價(jià)。首先,,采集成熟度為70%和90%的各80個(gè)肥城桃的高光譜信息,、SSC和硬度,,通過蒙特卡羅偏最小二乘法分析剔除異常值,利用光譜-理化值共生距離劃分樣本集,,采用競爭性自適應(yīng)權(quán)重采樣算法(CARS)和連續(xù)投影算法(SPA)選取特征波長,,并建立多元線性回歸(MLR)模型。研究表明:CARS-MLR模型性能優(yōu)于SPA-MLR模型,;預(yù)測SSC的CARS-MLR模型,,R2c和R2v分別為0.8191和0.8439,RPD為2.0,;預(yù)測硬度的CARS-MLR模型,,R2c和R2v分別為0.9518和0.8772,RPD為2.1,。然后,,基于CARS-MLR模型計(jì)算肥城桃每個(gè)像素點(diǎn)的SSC和硬度,生成可視化分布圖,,實(shí)現(xiàn)不同成熟度肥城桃SSC和硬度可視化檢測,。最后,利用順序前向選擇算法優(yōu)選特征波長,,建立人工神經(jīng)網(wǎng)絡(luò)成熟度預(yù)測模型,,獲得98.3%總識別準(zhǔn)確率。

    Abstract:

    Feicheng peach is prone to spoilage due to its surface color changing rapidly after harvest, which will degrade its quality. Hyperspectral imaging technology was used to detect the soluble solid content (SSC), firmness and maturity of Feicheng peach for improving its quality and price. There were 80 maturity 70% and 90% Feicheng peach were used for hyperspectral images (400~1000nm), SSC and firmness collection, respectively. These samples were split into calibration set and validation set with a ratio of 2∶1 by samples set partitioning based on joint X-Y distances method after the outliers were eliminated by using Monte Carlopartial least squares method. MLR detection models were established using feature wavelengths selected by competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), respectively. The more effective detection results was emerged by CARS-MLR model, with a determination coefficient of calibration set (R2c) of 0.8191, a determination coefficient of validation set (R2v) of 08439 and a residual prediction deviation (RPD) of 20 for SSC assessment, R2c of 0.9518, R2v of 0.8772 and RPD of 2.1 for firmness assessment. Visualization maps for SSC and firmness were generated by calculating the spectral response of each pixel on peach samples. Furthermore, the artificial neural network model was provided to predict the maturity of Feicheng peach using feature wavelengths selected by the sequential forward selection algorithm, with total recognition accuracy of 98.3%. It can be concluded that hyperspectral imaging technology can be applied to determine the SSC, firmness and maturity of Feicheng peach, laying a foundation for the online nondestructive quality monitoring and timely harvest of Feicheng peach. 

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邵園園,王永賢,玄冠濤,高沖,王凱麗,高宗梅.基于高光譜成像的肥城桃品質(zhì)可視化分析與成熟度檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(8):344-350. SHAO Yuanyuan, WANG Yongxian, XUAN Guantao, GAO Chong, WANG Kaili, GAO Zongmei. Visual Detection of SSC and Firmness and Maturity Prediction for Feicheng Peach by Using Hyperspectral Imaging[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(8):344-350.

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  • 收稿日期:2019-10-21
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  • 在線發(fā)布日期: 2020-08-10
  • 出版日期: 2020-08-10
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