【摘要】 嫁接愈合状态早期无损判别能提高嫁接愈合装置的利用率。以甜瓜嫁接苗为研究对象，获取嫁接后第1～10天嫁接部的高光谱数据，对原始光谱利用一阶导数和二阶导数、标准正态变换和去趋势处理、平滑21点、多元散射矫正等预处理方法，以及两种以上的方法组合，建立支持向量机、决策树和XGBoost 3种分类模型，优选模型对应的最优预处理算法，并利用主成分分析、竞争性自适应重加权算法、遗传算法和连续投影算法4种算法进行特征变量选择，最后利用特征变量建立分类判别模式。结果显示，一阶导数-遗传算法-XGBoost模型挑选出30个特征波长，预测准确率达到93%，效果最好。相比于嫁接后靠人工经验判断愈合状态，此方法在嫁接后第6天对嫁接苗的愈合状态进行判定，具有一定的理论和实践价值。更多还原

【Abstract】 Early non-destructive identification of grafting healing status can improve utilization rate of grafting healing devices.Taking melon grafted seedlings as research object, hyperspectral data of grafted area from the 1st to the 10th day after grafting was obtained.Raw spectral data were preprocessed by different methods,such as the first derivatives(FD)and second derivatives(SD),standard normal variation(SNV),detrend processing(Detrend),smooth 21 points(Smooth 21),multiple scattering correction(MSC),and combination of two methods as well.Establishing three classification models:support vector machine(SVM),decision tree and XGboost,principal component analysis(PCA),competitive adaptive reweighting sampling(CARS),genetic algorithms(GA)and continuous projections algorithm(SPA)were used to select characteristic variables corresponding to different classification models.Results showed that FD-GA-XGboost model selected 30 characteristic wavelengths,and prediction accuracy reached93%.Compared with traditional manual judgment of healing state 10 days after grafting,this method could judge healing state of grafted seedlings more accurately 6 days after grafting,which provided an important reference for production of melon grafted seedlings,and has certain theoretical and practical value.更多还原