基于软X射线成像的储粮害虫米象生长阶段检测

为了准确检测单粒小麦内部是否感染米象（Sitophilus oryzae），利用软X射线成像检测技术对感染不同生长阶段米象的小麦颗粒进行成像，试图通过图像分析来确定小麦内部米象的幼虫、蛹和成虫等不同生长阶段，并利用随机重复抽样建模来评价结果可靠性。通过对被感染米象虫卵不同天数小麦的图像分析发现，图像灰度分布直方图随感染天数变化明显，低灰度值区域（灰度值为10～102）的灰度区域像素点随感染天数增加而减少，中灰度（灰度值为103～162）和高灰度区域（灰度值为163～232)则随感染天数增加而增多。使用包括图像灰度分布和纹理特征等47个特征值，利用线性判别分析（Linear Discriminant Analysis，LDA）与二次判别分析（Quadratic Discriminant Analysis，QDA）建立判别模型，并通过多次随机重复抽样（1 000次）对模型预测效果进行评估分析。结果表明：在95% 置信区间下，在感染与未感染小麦的分类判别中，LDA的判别准确率都在76%以上，除幼虫外生长阶段判别正确率达到95%以上；而QDA的平均判别准确率较低且判别误差也相对较高。因此，该研究使用随机重复抽样方法LDA模型判别小麦是否受到米象感染和区分不同生长阶段是准确可靠的。

Detection of the growth stage of rice weevil as a stored-grain pest based on soft X-ray imaging

Abstract: In order to accurately detect whether the inside of wheat kernel was infected with rice weevil (Sitophilus oryzae), soft X-ray imaging detection technology was used to process the images of wheat grains infected with rice weevil at different growth stages. The different growth stages of rice weevil were determined by image, and the reliability of the results was evaluated by random repetition and discriminant analysis. Although some experimental research results show that the automatic recognition rate of pest infections detected by X-ray imaging could reach more than 90%, and even a high recognition rate of 100%, the actual operation shows that it is impossible to get a lower recognition rate by repeated detection. Due to the randomness of data collection, sampling, modeling, and other factors, these will bring uncertainty to the model prediction. For example, the initial value of the random number seed is not fixed, so the random division of the experimental data into a training dataset and a test dataset has absolute randomness, resulting in the prediction model will be different due to the change of the training dataset. Different prediction results are obtained on the same experimental data and the same algorithm. Therefore, it can be inferred that the evaluation parameters of the model should be within a certain range of values, rather than a single value. Because randomness is inherent, there is no way to avoid it. Random repeation and summary statistics of prediction performance measures are an excellent strategy.In this study, soft X-ray image technology was used to detect the hidden insect S. oryzae in wheat kernels. The different insect growth stages of S. oryzae were determined by taking pictures of S. oryzae in wheat kernels by soft X-ray. The gray histogram features of different infection days were extracted, it is found that the image gray level distribution of the image changed with the infection days, and the pixels in the gray area of the low gray area (gray value: 10-102) decreased with increase of the infection days, while the middle gray (gray value: 103-162) and high-gray areas (gray value: 163-232) increased with the increase of infection days. Based on 47 feature values, including 17 image grayscale histogram features and 30 texture features, a discriminant model was established by using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), and the prediction effect of the model was evaluated by multiple random repeated sampling (1000 times). The results showed that within the 95% confidence interval, the accuracy of LDA in the classification of infected and uninfected wheat was above 76%, and the accuracy of the growth stage except larvae was above 95%. However, the average accuracy of QDA was much lower, and the discrimination error of 1000 random samples was relatively higher. Therefore, it is accurate and reliable to use multiple random sampling and LDA classification methods to distinguish whether wheat is infested by S. oryzae and to distinguish different insect states of S. oryzae.