Application of near infrared hyperspectral imaging for identifying and quantifying red clover contained in experimental poultry refusals

Abstract For laying hens, free-range systems are promoted to improve both animal behavior and egg quality. In this context, estimation of feed intake, and particularly the proportion of herbage in the diet, has become a hot topic in poultry feeding management. During experiments, animals are housed in individual cages to measure daily ingestion, calculated by the difference between offered feed and refusals (non-ingested feed). Due to their natural behavior, laying hens tend to put offered feed on the ground, mixing grains and herbage with impurities (wood-chip litter and droppings) which leads to actual ingestion being underestimated. Manual sorting of mixed refusals is tedious. The aim of this study was thus to propose a procedure to identify herbage, red clover in this case, among impurities and to estimate the weight of red clover in entire mixed refusals by combining near infrared reflectance (NIR) imaging systems. A discriminant analysis (based on 8074 spectra) allowed each pixel corresponding to red clover to be identified. The results showed that more than 90 % of pixels were correctly classified. On the basis of the number of pixels predicted as red clover, a linear regression was built to convert pixels into red clover weight in order to recalculate the actual feed intake. A determination coefficient (R²) of 0.95 was achieved. Model validation was based on 12 composite refusals, and resulted in a determination coefficient of validation (R²v) equal to 0.83 and a root mean square error of prediction (RMSEP) equivalent to 3.4 g of dry matter (DM). The equation was considered satisfactory considering the possible bias in the manual sorting method. The main advantage of this procedure is the reduction in the time taken by the procedure by a factor of 4, while maintaining a high level of accuracy.