融合光谱形态特征的苹果霉心病检测方法

针对轻微霉心病和健康苹果光谱差异较小，致使基于可见/近红外特征光谱的检测方法对轻微霉心病检测准确率较低的问题。该研究将光谱形态特征与光谱特征融合的方法引入霉心病模型构建，建立了融合光谱形态特征的判别模型。以215个苹果可见/近红外光谱为样本，分析了不同预处理和特征提取组合对建模效果的影响，并完成了光谱特征的提取；分析健康果和霉心病苹果平均光谱的差异性，提取波峰、波谷等差异明显的光谱形态特征点，对比波段比、波段差和归一化强度差三类形态特征获取方法；最终建立光谱形态特征参数和光谱特征融合的苹果霉心病模型。试验结果表明，归一化预处理后提取的特征光谱和归一化强度差形态特征融合后模型判别准确率最高，在支持向量机模型中训练集、测试集判别准确率分别为98.6%和96.3%。特别是当发病程度小于10%时，该研究的判别模型准确率高于95%，表明通过融合光谱形态特征可以提升轻微病变霉心苹果的判别准确率。

Detection method for apple moldy cores based on spectral shape features

Moldy core is one of the most serious fungal diseases in apples. The visible/near infrared spectroscopy (VIS-NIR) technique has been a common-used approach to distinguish the apple moldy core. However, the better discriminant can be only confined to the severely diseased apples in the existing VIS-NIR, due to the smaller spectral difference between the mild mold core and healthy apples. It is a high demand to detect the mild mold core for early warning during apple production. In this study, an improved discriminant model was established to detect the apple moldy core using the spectral shape features, in order to significantly improve the detection accuracy. 215 well-developed red Fuji apples without external damage were selected from the orchard in Fufeng County, Baoji City, Shaanxi Province, China, in November 2020. The VIS-NIR (350-1100nm) information was first collected from these apples. The images were then captured from the cutting apples. The degree of moldy-core was determined to calculate the ratio of the mold core area to the apple profile before image pretreatment. The discriminative accuracy was firstly compared with the savitzky-golay convolution smoothing (S-G) after normalization (NOR), Multiplicative scatter correction (MSC) after NOR, and standard normal variate transform (SNV) after NOR. Secondly, the feature bands were extracted from the images using the combination of competitive adaptive reweighted sampling (CARS), and Successive projections (SPA). Thirdly, five peaks and valleys (at the wavelength of 639, 674, 705, 751, and 806 nm) were extracted from the average spectrum for the typical shape features. Band ratio (BR), band difference (BD), and normalized spectral intensity difference (NSID) were then analyzed to determine the spectral shape features (SSF) parameters with the highest discriminant accuracy. Finally, the optimal model was obtained between the partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). Results show that the NOR spectral pretreatment performed the best to extract the characteristic spectrum, whereas the NSID was the best among the three SSF parameters. The SVM model presented the highest discriminative accuracy with the training set of 98.6%, and the test set of 96.3%. There was 20% higher than the traditional model of the accuracy of the test set. Four models were used to evaluate the performance of model identification in the different degrees of moldy-core, including the build the modal with characteristic band, the correct spectrum with apple diameter, compensate with the direction of detection, and merge the spectral shape seatures. Once the degree of moldy-core was greater than 10%, the accuracies of these models were improved significantly, except for the build the modal with characteristic band. When the degree of moldy-core was less than 10%, only the Merged Spectral Shape Features Model performed a high discriminant accuracy of higher than 95%, which was 43.5% higher than the build the modal with characteristic band. In the case of the moldy-core degree less than 6%, the discrimination accuracy of merge the spectral shape features reached 95.8%, which was 62.5%, and 12.5% higher than the build the modal with characteristic band, and the correct spectrum with apple diameter, respectively. Consequently, the discrimination model merged with the NISD in the input of the apple mold core can be expected to greatly improve the discrimination accuracy, particularly for the mild mold core. The improved model merged with the spectral shape features can be an effective way to accurately discriminate the apple moldy core.