No abstract

Subclinical mastitis is an important udder disease that negatively affects both the animal health and reduces profitability in dairy farms. The increasing performance of thermal cameras over time and their usability in different areas increase their use in livestocks. Infrared thermography (IRT) technology is a noninvasive method that can estimate the surface temperature of objects. The objective of this study was to evaluate early detection of mastitis in Holstein-Friesian dairy cattle by using both udder surface temperatures (Tmax) from images obtained with the help of a FLIR One Pro thermal camera and some parameters such as Lab (CIE L*, a*, b*), HSB (Hue, Saturation, Brightness), RGB (Red, Green, Blue) by processing thermal images with the help of ImageJ program via classification and regression tree (CART) analysis. According to California Mastitis Test CMT by using CART analysis in this study, 64.9% of cows with udder surface temperature lower than 38.85 were healthy, and 73.3% of cows higher than 38.85 were determined as unhealthy. As for SCC, 77.6% of cows with udder surface temperature lower than 38.65 were healthy and 58.6% of cows with higher than 38.65 were determined as unhealthy. The areas under ROC (AUC) were found to be statistically significant in the diagnosis of subclinical mastitis. (P<0.01) The sensitivity and specificity of the CART algorithm for CMT and SCC diagnostic tests were 85.42%, 81.48% and 90.20%, 80.39%, respectively. There was no significant difference between SCC and CMT tests in the area under the ROC curve (P>0.05). As a result, IRT technology can be used as a useful diagnostic tool in the early detection of mastitis.

This study's objective is to compare the performances of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Bayesian Regularization Neural Network (BRNN) algorithms, which are some data mining algorithms used in final fattening live weight prediction. As the independent variable in the design of the algorithms, some body characteristics taken before fattening of 54 heads of Anatolian Merino lambs, are withers height (WH), rump height (RH), body length (BL), chest girth (CG), Leg girth (LG), and chest depth (CD) was used. The mean±standart errors for the body characteristics of Anatolian Merino lambs were determined to be 63.481±0.538, 63.315±0.501, 78.930±1.140, 60.037±0.549, 47.704±0.543, and 29.926±0.377, respectively. The mean initial live weight (ILW) and the mean final live weight (FLW) were found as 35.89±0.84 and 49.49±0.88 kg, respectively. There was difference of 13.60 kg between ILW and FLW means. The ILW and FLW were shown to positively correlate with body characteristics, and this correlation was statistically significant (P

Bu çalışmanın amacı 0-12 aylık yaşta farklı büyüme ve gelişme dönemindeki sığırların bazı vücut ölçümlerinden canlı ağırlık tahmininde kullanılan veri madenciliği algoritmalarının karşılaştırılmasıdır. Çalışmada 24 baş dişi ve 18 baş erkek olmak üzere toplamda 42 baş sığıra ait kimi vücut ölçülerinden göğüs çevresi (GÇ), göğüs derinliği (GD), vücut uzunluğu (VU), cidago yüksekliği (CY), sağrı yüksekliği (SY) ile cinsiyet ve yaş özelliği bağımsız değişken, canlı ağırlık ise bağımlı değişken olarak ele alınmıştır. Vücut ölçülerinden canlı ağırlığının tahmin edilmesinde ise veri madenciliği algoritmalarından Çoklu Doğrusal Regresyon (MLR), Rastgele Orman (RF), Karar Ağacı (DT) ve En Yakın Komşu (kNN) algoritmaları çapraz doğrulama (cross-validation) 5 alınarak kullanılmıştır. Vücut ölçüleri ile canlı ağırlık (CA) arasında pozitif bir korelasyon olduğu tespit edilmiştir (P

No abstract