Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures

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dc.authoridozden, cevher/0000-0002-8445-4629
dc.authoridbulut, mutlu/0000-0002-4673-3133
dc.authoridBOGA, MUSTAFA/0000-0001-8277-9262
dc.authoridCANGA BOGA, DEMET/0000-0003-3319-7084
dc.contributor.authorOzden, Cevher
dc.contributor.authorBulut, Mutlu
dc.contributor.authorBoga, Demet Canga
dc.contributor.authorBoga, Mustafa
dc.date.accessioned2024-11-07T13:34:22Z
dc.date.available2024-11-07T13:34:22Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSimple Summary This study employs Fully Convolutional Regression Networks (FCRN) and U-Shaped Convolutional Network for Image Segmentation (U-Net) architectures tailored to the dataset containing dropping images of dairy cows collected from three different private dairy farms in Nigde. The main purpose of this study is to detect the number of undigested grains in dropping images in order to give some useful feedback to raiser. It is a novel study that uses two different regression neural networks on object counting in dropping images. To our knowledge, it is the first study that counts objects in dropping images and provides information of how effectively dairy cows digest their daily rations. Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.
dc.identifier.doi10.3390/vetsci10010032
dc.identifier.issn2306-7381
dc.identifier.issue1
dc.identifier.pmid36669033
dc.identifier.scopus2-s2.0-85146741030
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/vetsci10010032
dc.identifier.urihttps://hdl.handle.net/11480/15945
dc.identifier.volume10
dc.identifier.wosWOS:000916058000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofVeterinary Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectimage processing
dc.subjectdeep learning
dc.subjectlivestock
dc.subjectimages of feces
dc.subjectindigestible parts
dc.titleDetermination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures
dc.typeArticle

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