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

Type de document
journalArticle
Langue source
-- Langue source --
Titre
Application of near infrared hyperspectral imaging for identifying and quantifying red clover contained in experimental poultry refusals
Titre français
Titre anglais
Auteur(s)
  • TOSAR V.
  • FERNÁNDEZ PIERNA J. A.
  • DECRUYENAERE V.
  • LARONDELLE Y.
  • BAETEN V.
  • FROIDMONT E.
Editeur(s)
Autre(s)
Id
V3UQWLFP
Version
3352
Date ajout
6 avril 2021 17:59
Date modification
20 avril 2021 08:52
Résumé
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.
Note
None
CRAW tags
  • AB - Utile à l'AB
  • CRA-W
  • FREDO alimentation animale
  • FREDO fourrage et prairie
  • FREDO mode élevage, bien-être et qualité
  • FREDO qualité des produits
  • FREDO santé animale
  • FREDO technologie et innovation
  • GEO Belgique
  • GEO Wallonie
  • NIR
  • poule pondeuse
  • volaille
  • élevage
WEB tags
  • feed intake
  • imaging
  • impurity
  • multivariate analysis
  • refusal
Titre de la publication
Animal Feed Science and Technology
Volume
273
Pages
114827
Date caractères
March 1, 2021
Date publication
1 mars 2021
Doi
10.1016/j.anifeedsci.2021.114827 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique
Issn
0377-8401 L’ISSN est un code de 8 chiffres servant à identifier les journaux, revues, magazines, périodiques de toute nature et sur tous supports, papier comme électronique.