基于光谱的春季育苗移栽期番茄苗健壮度量化检测

为了筛选影响春季育苗移栽期番茄穴盘苗健壮程度的关键指标，并实现其快速无损检测，测定了5项秧苗指标，经向量归一化预处理并采用独立性权系数法确定各指标权重，并根据权重结果挑选出包含信息较全面，影响较大的两个指标：叶绿素和干质量。两项指标所组成的简化秧苗评价值可以近似表示综合评价值，相关系数r为0.92，大大减少了品质检测所需的指标量，并可以很好的表征春季育苗移栽期番茄苗的健壮度。提取了各穴盘苗的可见-近红外光谱数据，经去噪和多元散射矫正（MSC）预处理，消除了由光散射等带来的光谱干扰信息，相较原始光谱信息更具可利用性。随后采用光谱-理化值共生距离（SPXY）算法对样本集进行划分，利用波段值和评价值两种变量同时计算样本间距离，以最大化表征样本分布，提高样本差异性和代表性。采用竞争性自适应重加权算法（CARS）和无信息变量消除连续投影算法（UVE-SPA）优选光谱特征波数，降低光谱数据维度，得到了更能体现光谱特征的简化光谱信息，减少了冗余信息对建立模型精准度和分析速度的影响。最后应用偏最小二乘支持向量机（LS-SVM）和基于U-Net模型改造的卷积神经网络(CNN)，以预处理后的光谱数据和提取特征波长后的光谱数据分别作为模型的输入，建立了光谱数据与综合评价值的非线性映射模型，并进行对比选优。结果显示：应用UVE-SPA预处理方法筛选出的波段，光谱信息更加丰富有效；两种预处理后的优选波段所建模型回归效果整体优于全波段建立的模型；CNN模型的建模效果整体优于LS-SVM模型，且UVE-SPA-CNN模型对光谱数据和秧苗评价值的回归分析效果最好，其建模集和预测集的相关系数r分别为0.988和0.946，均方根误差RMSE分别为0.085和0.025，为直接利用光谱数据获取融合了多种因素的番茄秧苗评价值，从而判别秧苗健壮度提供了理论依据。

Study on Quantitative Detection of Tomato Seedling Robustness in Spring Seedling Transplanting Period Based on VIS-NIR Spectroscopy

To screen the key indicators that affect the robustness of tomato plug seedlings during the spring nursery and transplanting period and realize its rapid non-destructive testing, this paper measured 5 seedling indicators, then used vector normalization and the independence weight coefficient method to determine each indicator. According to the weighting results, two indicators containing more comprehensive information and greater influence are selected: chlorophyll and dry quality. The simplified seedling evaluation value composed of the two indicators can approximate the comprehensive evaluation value. The correlation coefficient is 0.92, which greatly reduces the number of indicators required for quality testing, and can well represent the robustness of tomato seedlings during the spring seedling transplanting period. At the same time, the visible-near infrared spectrum data of each plug seedling is extracted and pre-processed by denoising and multi-scattering correction (MSC). This way, it can eliminate the spectral interference information caused by light scattering and make it more usable than the original spectral information. Subsequently, the spectrum-physical and chemical value symbiosis distance (SPXY) algorithm is used to divide the sample set. The distance between the samples is calculated using two variables of the band value and the evaluation value to maximize the characterization of the sample distribution to improve the difference and representativeness of the samples. Secondly, the competitive adaptive weighting algorithm (CARS) and the uninformative variable elimination-successive projections algorithm (UVE-SPA) are used to optimize the spectral feature wave number, reduce the spectral data dimension and obtain simplified spectral information that can better reflect the spectral characteristics and reduce redundancy. Finally, partial least squares-support vector machine (LS-SVM) and convolutional neural network (CNN) based on U-Net model transformation are applied. After extracting the characteristic wavelength, the preprocessed spectral data and the spectral data are respectively used as the input of the model and established a non-linear mapping model of spectral data and comprehensive evaluation value. We can carry out comparison and selection. The results show that the spectral information of the bands filtered by the UVE-SPA preprocessing method is more abundant and effective. The regression effects of the models built for the two preprocessed optimal bands are overall better than the models built for the full bands; the modeling effect of the CNN model is overall. It is better than the LS-SVM model, and the UVE-SPA-CNN model has the best effect on the regression analysis of spectral data and seedling evaluation values. The correlation coefficients of the modeling set and prediction set are 0.988 and 0.946, respectively, and the values of the root mean square error are 0.085 and 0.025, respectively, which provide a theoretical basis for directly using spectral data to obtain the evaluation value of tomato seedlings that incorporate multiple factors, thereby judging the robustness of the seedlings.