In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals. The optimal band was selected for the original and pre-processed spectra which were treated by multiple scatter correction (MSC) and the Savitzky-Golay first derivative (SG 1st). Five characteristic wavelength methods were compared: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable important in projection (VIP), and random frog (RF). Partial least square-discriminant analysis (PLS-DA) and K-nearest neighbor (KNN) built the vitality estimation model based on different data sets, and GA + PLS-DA constructed the optimal model with the highest accuracy. According to the weight coefficient and reflectance of the characteristic band extracted by the GA, the reflectance curves of different levels over time were plotted. The data of 0 h was employed to establish the vitality forecast model. The forecast model had a high recognition rate, with PLS-DA exceeding 99% and KNN exceeding 85%. This indicated that hyperspectral imaging of seed germination processes could achieve non-destructive estimation of Q. variabilis seed vitality, and accurate prediction in a shorter time is feasible.

在这项研究中，估计和预测不同活力栎属的可行性通过高光谱成像技术研究了种子。人工加速衰老有助于实现四个生命力水平的划分。在发芽的最初10小时内，以一小时的间隔连续收集高光谱数据。为原始光谱和预处理光谱选择最佳波段，并通过多重散射校正（MSC）和Savitzky-Golay一阶导数（SG 1st）对其进行处理。比较了五个特征波长方法：连续投影算法（SPA），竞争性自适应加权采样（CARS），遗传算法（GA），重要投影变量（VIP）和随机青蛙（RF）。偏最小二乘判别分析（PLS-DA）和K近邻（KNN）建立了基于不同数据集的生命力估算模型，而GA + PLS-DA则以最高的精度构建了最优模型。根据遗传算法提取的特征带的权重系数和反射率，绘制了不同水平随时间变化的反射率曲线。采用0 h数据建立生命力预测模型。该预测模型具有较高的识别率，PLS-DA超过99％，KNN超过85％。这表明种子发芽过程的高光谱成像可以实现对种子发芽的无损估计。PLS-DA超过99％，KNN超过85％。这表明种子发芽过程的高光谱成像可以实现对种子发芽的无损估计。PLS-DA超过99％，KNN超过85％。这表明种子发芽过程的高光谱成像可以实现对种子发芽的无损估计。Q. variabilis种子的活力，在较短的时间内进行准确的预测是可行的。