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

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Özden C., Bulut M., Çanga Boğa D., Boğa M.

Veterinary Sciences, vol.10, no.1, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.3390/vetsci10010032
  • Journal Name: Veterinary Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: image processing, deep learning, livestock, images of feces, indigestible parts
  • Akdeniz University Affiliated: Yes

Abstract

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.