基于高光谱反射成像和深度学习方法对不同冻害程度玉米种子进行识别

摘要：种子冻害是一种农业灾害，对种子质量保证影响很大。本文研究了将高光谱成像与深度卷积神经网络（（ DCNN）相 结合对不同冻害玉米种子进行分类的可行性。首先，获取玉米种子在5种不同冷冻温度下400 1000 nm的高光谱图像， 然后从450–979 nm波长范围内的胚高光谱图像区域中提取平均光谱。接下来，针对五类（（ 5种冻害状况）和四类（（ “无冻 害”、“轻微冻害”、“中度冻害”和“严重冻害”）分类，开发了四种模型（（ K最近邻（（ KNN）、支持向量机（（ SVM）、极限学 习机（（ ELM）和DCNN），并计算了评价指标（（ 准确度、灵敏度、特异性和精确度）的值以进行比较。结果表明，DCNN 模型的五类分类准确率最令人满意，分别为100％（训练集）、96.9％（验证集）和97.5％（测试集），四类分类准确率均 为100％（训练、验证和测试集），DCNN模型在评价指标中的表现也最好。最后，根据DCNN的结果生成了可视化分类 图。结果表明，高光谱成像和DCNN可以提供一种快速、廉价地检测玉米种子冻害的新方法。

关键词：识别 冻害玉米种子 高光谱成像 深度卷积神经网络 性能评价指标 图像可视化

期刊：Food Analytical Methods

DOI：https://DOI.org/10.1007/s12161-020-01871-8

设备：GaiaField

Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method

Abstract：Seed freezing damage is anagricultural disaster, which has a great impact on seed quality assurance. The feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) to classify different freeze-damaged corn seeds was studied in this paper. At first, the hyperspectral images of corn seeds subjected to five different freezing temperatures at 400 1000 nm were acquired, and then the average spectra were extracted from the region of embryo hyperspectral images over the wavelength range of 450–979 nm. Next, four models (K nearest neighbors (KNN), support vector machine (SVM), extreme learning machine (ELM), and DCNN) were developed for five-category (5 frozen conditions) and four-category (“no freezing,” “slight freezing,”“moderate freezing,” and “severe freezing”) classifications, and the values of the evaluation indexes (accuracy, sensitivity, specificity, and precision) were calculated for comparison. The results show that DCNN model had the most satisfactory result with accuracy rates of 100% (training set), 96.9% (validation set), and 97.5% (testing set) for five-category classification, with accuracy rates of 100% (training, validation, and testing set) for four-category classification, and DCNN model also had the best performance in the evaluation indexes. At last, the visual classification map was generated according to the results of DCNN. It shows that hyperspectral imaging and DCNN can provide a novel method to detect the freezing damage of corn seeds quickly and inexpensively.

Keywords：Identification .Freeze-damagedcornseed .Hyperspectralimaging .Deepconvolutionalneuralnetwork .Performance evaluation index . Image visualization