农业工程学报
農業工程學報
농업공정학보
2015年
15期
285-292
,共8页
图像处理%模型%无损检测%高光谱%种蛋%相关向量机%支持向量机
圖像處理%模型%無損檢測%高光譜%種蛋%相關嚮量機%支持嚮量機
도상처리%모형%무손검측%고광보%충단%상관향량궤%지지향량궤
imaging processing%models%nondestructive examination%hyperspectral image%hatching eggs%RVM%SVM
为了尽可能早的检测出无精蛋和受精蛋，该文提出采用透射高光谱成像技术，融合图像和光谱信息，对其受精信息进行检测。利用高光谱图像系统采集孵化前种蛋在400～1000 nm的高光谱图像，提取图像特征（长短轴之比、伸长度、圆度、蛋黄面积与整蛋面积之比）；筛选出400～760 nm的波段，通过Normalize预处理结合相关系数法提取155个光谱特征变量；运用主成分分析法对图像和光谱的融合信息进行降维，采用相关向量机（relevance vector machine，RVM）分别建立基于图像、光谱和图像-光谱融合信息的受精蛋和无精蛋分类判别模型，并与支持向量机（support vector machine， SVM）模型进行比较，RVM模型检测正确率分别为90%，91%，96%；测试集检测时间分别为0.6619，1.0821，0.5016 s。SVM模型检测正确率分别为84%，90%，93%；测试集检测时间分别为5.9386，5.9886，5.6672 s。结果表明，基于图像-光谱融合所建立的模型优于单一信息的模型，在分类精度上，采用RVM分类精度高于SVM的分类精度；在分类时间上， RVM的分类时间比SVM短，因此，利用高光谱融合信息和相关向量机可以提高种蛋检测精度，研究结果为孵前无精蛋和受精蛋的在线实时检测提供参考。
為瞭儘可能早的檢測齣無精蛋和受精蛋，該文提齣採用透射高光譜成像技術，融閤圖像和光譜信息，對其受精信息進行檢測。利用高光譜圖像繫統採集孵化前種蛋在400～1000 nm的高光譜圖像，提取圖像特徵（長短軸之比、伸長度、圓度、蛋黃麵積與整蛋麵積之比）；篩選齣400～760 nm的波段，通過Normalize預處理結閤相關繫數法提取155箇光譜特徵變量；運用主成分分析法對圖像和光譜的融閤信息進行降維，採用相關嚮量機（relevance vector machine，RVM）分彆建立基于圖像、光譜和圖像-光譜融閤信息的受精蛋和無精蛋分類判彆模型，併與支持嚮量機（support vector machine， SVM）模型進行比較，RVM模型檢測正確率分彆為90%，91%，96%；測試集檢測時間分彆為0.6619，1.0821，0.5016 s。SVM模型檢測正確率分彆為84%，90%，93%；測試集檢測時間分彆為5.9386，5.9886，5.6672 s。結果錶明，基于圖像-光譜融閤所建立的模型優于單一信息的模型，在分類精度上，採用RVM分類精度高于SVM的分類精度；在分類時間上， RVM的分類時間比SVM短，因此，利用高光譜融閤信息和相關嚮量機可以提高種蛋檢測精度，研究結果為孵前無精蛋和受精蛋的在線實時檢測提供參攷。
위료진가능조적검측출무정단화수정단，해문제출채용투사고광보성상기술，융합도상화광보신식，대기수정신식진행검측。이용고광보도상계통채집부화전충단재400～1000 nm적고광보도상，제취도상특정（장단축지비、신장도、원도、단황면적여정단면적지비）；사선출400～760 nm적파단，통과Normalize예처리결합상관계수법제취155개광보특정변량；운용주성분분석법대도상화광보적융합신식진행강유，채용상관향량궤（relevance vector machine，RVM）분별건립기우도상、광보화도상-광보융합신식적수정단화무정단분류판별모형，병여지지향량궤（support vector machine， SVM）모형진행비교，RVM모형검측정학솔분별위90%，91%，96%；측시집검측시간분별위0.6619，1.0821，0.5016 s。SVM모형검측정학솔분별위84%，90%，93%；측시집검측시간분별위5.9386，5.9886，5.6672 s。결과표명，기우도상-광보융합소건립적모형우우단일신식적모형，재분류정도상，채용RVM분류정도고우SVM적분류정도；재분류시간상， RVM적분류시간비SVM단，인차，이용고광보융합신식화상관향량궤가이제고충단검측정도，연구결과위부전무정단화수정단적재선실시검측제공삼고。
It is one of difficult problems to be resolved in egg hatching industry to identify the fertile information of hatching eggs and eliminate infertile eggs prior to the incubation. Many infertile eggs have been wasted in the process of incubation every year, which has caused considerable economic loss. The existing domestic infertile egg detection mainly depends on traditional manual candle method. However, this detection method requires high intensity of labor and is time-consuming. In addition, the result of detection is subjective and its accuracy can not be guaranteed. The detection of infertile eggs prior to incubation can improve the economic efficiency of incubation and the quality of egg processing in late period, and it can bring considerable economic benefits. This paper proposed that the hyperspectral imaging technology consisting of image and spectral information and the relevance vector machine (RVM) were used for detecting the fertile information of eggs before incubation. To build a hyperspectral transmission image acquisition system, the light source, the light intensity, the resolution, the exposure time, the platform moving speed and other parameters were adjusted when the images of hyperspectral instrument were captured. Ultimately, the exposure time of the camera was determined as 0.1 s, the resolution of image as 400×400 pixels, and the platform moving speed as 1.7 mm/s. Before hatching eggs incubation, hyperspectral images system was used to acquire the images of hatching eggs between 400 and 1000 nm. The characteristic information of the ratios of length to short axis, the elongation, the roundness and the ratios of the yolk area to the whole area was extracted based on the images. Based on the comparison of the calibration results among 3 waveband regions (400-760, 760-1000, and 400-1000 nm), the visible light in band range of 400-760 nm was chosen to classify actual type of hatching eggs. Different spectra pretreatment methods were used to analyze the spectra, e.g. multiplicative scatter correction (MSC), normalize, standard normal variate transformation (SNV), first derivative (FD), MSC+FD, SNV+FD, normalize+FD, among which the normalized pretreatment method was the most effective, and its classification accuracy was better than other methods. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 wavebands through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimension of image-spectrum fusion information which consisted of 4 image characteristic variables and 155 spectral characteristic variables, and then the top 6 principal components were extracted. According to the distribution principle of 2:1 for 300 hatching eggs, the numbers of eggs for training set and testing set were 200 and 100 respectively. RVM and support vector machine (SVM) were used to establish classification models, which were based on image, spectrum and image-spectrum fusion information respectively. The accuracies of the RVM models were 90%, 91% and 96% respectively, while the accuracies of the SVM models were 84%, 90% and 93% respectively. The cost time of the RVM models was 0.6619, 1.0821 and 0.5016 s respectively, while that the SVM models was 5.9386, 5.9886 and 5.6672 s respectively. The experimental results showed that the model based on image-spectrum fusion information was better than the single information model; the RVM model was superior to the SVM model for detecting fertile information of hatching eggs before incubation; and the cost time of RVM model was shorter than that of SVM model. The fertile and infertile eggs were identified very quickly. This project implementing would provide theoretical basis for the real-time online detection and testing of hating eggs for the instrument. Thus using hyperspectral fusion information and RVM can improve the detection accuracy of hatching eggs before incubation.
