Non-Destructive Detection of Egg Fertilization Status Based on Hyperspectral Diffuse Reflectance

During the incubation period of the breeding eggs, a lot of workforce and material resources are consumed, and the eggs during the incubation period cannot be guaranteed to be healthy fertilized eggs. It is necessary to quickly and accurately select the infertile eggs and dead sperm eggs in the early stage of the breeding eggs to reduce production costs. We take Bailaihang eggs as the research object and use a hyperspectral sorter to collect 119 fertilized, unfertilized, and dead eggs in batches with hyperspectral data in the range of 382 similar to 1 026 nm. The original spectrum is corrected by the black and white correction method to obtain the diffuse reflectance of the egg. After experimental comparison and actual production needs, 3d and 5d spectral data are selected as modeling data. We also propose a method to convert spectral data into image data, which achieves the effect of visualizing spectral vector data under the premise of maximizing the guarantee of the original spectral data and can be effectively combined with deep learning image recognition algorithms. We use SPA and CARS to filter the spectral bands and establish a discriminant model based on the full band, the characteristic wavelengths filtered by CARS, the characteristic wavelengths filtered by SPA and SVM, the Random Forest algorithm and AlexNet, MobileNet network. The highest accuracy rate of AlexNet-5d Full Wave Bands is 93.22%. By comparing the experimental results of the data after the screening of different characteristic wavelength algorithms, it is found that the modeling effect of the characteristic wavelengths filtered by the SPA algorithm is better than that of CARS. The accuracy of the SVM-SPA3d model is 91.52%. The accuracy of the RandomForest-SPA3d model is 89.83%. The accuracy of the AlexNet-SPA3d model is 89.83%. The results show that the characteristic wavelengths screened by SPA can save more effective information about the difference inbreeding egg information. The research results in this paper show that the diffuse reflectance spectrum values of batches of hatching eggs are collected by a hyperspectral sorter first, and then the original spectral diffuse reflectance data is converted into image data. Combining image data with deep learning image recognition algorithms is feasible to accurately and non-destructively identify the fertilization state of eggs. This study provides technical support for subsequent related automated batch testing.