利用高光谱技术预测采前猕猴桃干物质含量的可行性试验

为实现采前猕猴桃果实干物质含量的实时、连续、大量预测，该研究就利用高光谱技术在室外开放环境下进行采前猕猴桃干物质含量预测试验。该研究以种植于贵州省息烽县、修文县的贵长猕猴桃为试验对象，利用高光谱相机获取采前猕猴桃样本果实的高光谱数据；对原始数据进行白板校正、ROI（Region of Interest）裁剪、多元散射校正等处理,获得样本果实光谱反射率曲线；根据光谱曲线特征，选取特征波段，构建多光谱指数；将样本果实划分为训练集、测试集；利用多光谱指数将训练集样本果实特征波段光谱反射率换算为指数值，分析指数值与干物质含量的相关性，确定最优指数，将其拟合公式作为干物质含量预测模型，利用测试集计算误差情况并验证模型预测效果。结果表明，果实干物质含量高，光谱反射率低，反之则光谱反射率高；根据特征波段构建的拟合效果最佳的多光谱指数，所对应的干物质含量预测模型决定系数为0.88，预测值最大绝对误差为1.31%，最大相对误差为8.23%，相对误差均值为3.13%，均方根误差为0.65%，具有较好的预测效果。试验证明，利用高光谱技术进行采前猕猴桃果实干物质含量预测是可行的。

Feasibility of estimating the dry matter content of kiwifruits before being harvested using hyperspectral technology

To achieve real-time, continuous, and batch prediction of dry matter content in pre-harvest kiwifruits, to the researchers verified the feasibility of using hyperspectral technology of dry matter content prediction for pre-harvest kiwifruits in field environment. The study conducted an experiment of hyperspectral data collection for pre-harvest kiwifruit on September 2021, in the main kiwifruit production areas of Xifeng and Xiuwen county, Guizhou Province. The hyperspectral data of pre-harvest kiwifruit samples were obtained with a hyperspectral camera, and the spectral bands were set from 500 to 900 nm with a spectral resolution of 2 nm, which had 193 spectral bands totally. The raw data were subjected to whiteboard correction, ROI (Region of Interest) cropping, and multiple scattering correction to obtain the spectral reflectance curves of the sample fruits. The characteristic bands were selected according to the characteristics of the spectral curves and the spectral absorption characteristics of water and chlorophyll, and the sample fruits were divided into a training set and a test set. The multispectral index was used to convert the spectral reflectance of the sample fruits in training set into index values. Through analyzing the correlation between index values and dry matter content, the optimal index was determined, and its fitting formula was used as the prediction model of dry matter content. The coefficient of determination, root mean square error, absolute error, relative error, and relative error mean were used as test indicators to calculate the error situation and verify the prediction effect of the model using the test set of sample fruits. The results showed that the spectral curves of all samples were similar, with high dry matter content of fruits having low spectral reflectance and low dry matter content of fruits having high spectral reflectance. The identified characteristic bands were 671.14, 745.08, 753.32, 805.14 and 886.93 nm, and five multispectral indices (I1-I5) were constructed according to the characteristic bands. Among the five multispectral indices, the determination coefficients of of I3, I4 and I5 reached above 0.8, and the highest reached 0.88 (I4). The prediction model of dry matter content for pre-harvest kiwifruit was established using this best-fitting multispectral index. In terms of the evaluation on the test set, the maximum absolute error of the model was 1.31% and the maximum relative error was 8.23%; the minimum absolute error was 0.03% and the minimum relative error was 0.23%. In terms of the overall prediction effect of sample fruits in the test set, the mean relative error of the prediction results was 3.13%, and the root mean square error was 0.65%. Compared with a previous study on the prediction of dry matter content of pre-harvest mango fruit using hyperspectral technology, which coefficient of determination was 0.64, and root mean square error was 1.08%. The proposed model also showed good accuracy. The experiment proved that it is feasible to use hyperspectral technology for dry matter content prediction on pre-harvest kiwifruit.