Classification and discrimination of chicken feces using visible-near infrared spectroscopy

Chicken disease identification with manual monitoring cannot fully meet the large-scale production in livestock and poultry breeding industry, due to slow speed and chickens prone to cross-infection. The characteristics of chicken feces can be one of the important indicators to reflect the health status of chickens. The different colors and traits of feces are associated with different chicken diseases. In this study, a classification and discrimination model was proposed for the chicken feces using visible-near-infrared spectroscopy. A research foundation was laid for the final realization of early warning of chicken diseases. Four types of typical chicken feces sample were selected, including the normal, red blood streak, green and fodder feces. The spectral data of samples was scanned in the 400-900 nm band. Each type of chicken feces samples was divided into the correction sets (162 samples) and test sets (53 samples) at 3:1, according to the principle of randomness. Multivariate scattering correction, Savitzky-Golay convolution smoothing, and Z-scores normalization were used to preprocess the data. A PLS-DA (partial least squares discriminant analysis) model was then established to select the optimal pretreatment, according to the evaluation index. An improved shuffled frog leaping algorithm was proposed to further optimize the existing partial least squares discriminant analysis model. The calculation rate of the model was effectively improved to obtain a more accurate number of principal factors. The number of iterations was determined to minimize the root mean square error of cross-validation. The PCA (principal component analysis), CARS (competitive adaptive reweighted sampling), and ISFLA (improved shuffled frog leaping algorithm) were used to reduce the data dimensionality of the processed samples using the preferred pretreatment. Finally, a categorical discriminant model was established after optimization. The results showed that the PLS-DA model established by the SG convolution smoothing-MSC-Z-Scores shared the better performance of normal stool sample data, and for the three types of abnormal samples, the PLS-DA model established by MSC performed better. The accuracies of the test sets were achieved at 74.07%, 91.98%, 96.15%, and 100%, respectively, for four types of feces samples. The optimal data preprocessing was determined using model evaluation. The ISFLA-PLS-DA was used to distinguish between the normal, red-blooded feces and green feces, where the accuracies of the test set were 92.27%, 92.59% and 100%, respectively. More importantly, the PCA-PLS-DA should be used for the discrimination in the fodder feces, where the accuracy of the test set was 100%. The dimensionality of the data was reduced by feature extraction to construct the lightweight PLS-DA model. The accuracy of the correction set was significantly improved in each typical stool sample. Specifically, the accuracies of the correction set were significantly improved before optimization, which increased by 7.70, 0.61, and 3.85 percentage point, respectively, of normal, red-blooded feces and green feces. Therefore, rapid, accurate and non-contact detection was achieved in the different types of typical chicken feces using visible-near-infrared spectroscopy detection combined with the characteristic wavelength selection and PLS-DA discrimination. The finding can also provide the strong reference for the intelligent detection technology of chicken disease.