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Tarr, Bence
Szabó, István
Tőzsér, János
2025-08-11T05:50:36Z
2025-08-11T05:50:36Z
2025
http://hdl.handle.net/20.500.14044/32102
Precision agriculture brings new artificial intelligence techniques closer to everyday farming. Agriculture historical data is easily available, so using this data to teach a machine-learning model, offers various opportunities to enhance farming efficiency. In our study, we develop a machine learning model to estimate some linear traits of Limousin sires (sore for muscularity, length of the rump, muscularity of breast and muscularity of the width of rump), based on a phenotypic score, using artificial intelligence, in Hungary. Phenotypic scores are usually given by the experts in field. Before scoring, many measurements are made on the animals, which takes time and places a high stress on the cattle. A hands-on prediction application can make the whole process faster, and more comparable, regardless of the expert who created the scoring. We found that after collecting sufficient data from previous observations it is possible to train specifically selected artificial intelligence (AI) algorithms to predict linear traits in Limousin breeding bulls. Machine learning (ML) was used to predict the score values for muscularity, length of the rump, muscularity of the breast and muscularity of the width of the rump for this study. We found no similar experiments for the usage of AI algorithms to predict these variables. The coefficient of determination (R 2) of the algorithm, in this study, provided the following range values: (R 2=0.77 to 0.86).hu_HU
dc.formatPDFhu_HU
enhu_HU
Body Conformation Scoring of Cattle, using Machine Learninghu_HU
Open accesshu_HU
Óbudai Egyetemhu_HU
Budapesthu_HU
Óbudai Egyetemhu_HU
Agrártudományok - állattenyésztési tudományokhu_HU
artificial intelligencehu_HU
machine learninghu_HU
Limousinhu_HU
bullshu_HU
type traitshu_HU
Tudományos cikkhu_HU
Acta Polytechnica Hungaricahu_HU
local.tempfieldCollectionsFolyóiratcikkekhu_HU
10.12700/APH.22.3.2025.3.2
Kiadói változathu_HU
12 p.hu_HU
3. sz.hu_HU
22. évf.hu_HU
2025hu_HU
Óbudai Egyetemhu_HU


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