HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Interpretive Summary Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Pastell, M. E. Articles by Kujala, M. Search for Related Content PubMed PubMed Citation Articles by Pastell, M. E. Articles by Kujala, M.

A Probabilistic Neural Network Model for Lameness Detection

M. E. Pastell^*,1 and M. Kujala

^* Department of Agrotechnology, PO Box 28 (Koetilantie 3), and
Faculty of Veterinary Medicine, PO Box 66, FI-00014 University of Helsinki, Finland

¹ Corresponding author: matti.pastell{at}helsinki.fi

A 4-balance system for measuring the leg-load distribution of dairy cows during milking to detect lameness was developed. Leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 mo. Cows were scored weekly for locomotion, and lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and number of kicks during milking were calculated. To develop an expert system to automatically detect lameness cases, a model was needed, and a classifying probabilistic neural network model was chosen for the task. The data were divided into 2 parts and 5,074 measurements from 37 cows were used to train a classifying probabilistic neural network model. The operation of the model was evaluated for its ability to detect lameness in the validating data set, which had 4,868 measurements from 36 cows. The model was able to classify 96.2% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements (equal to the number of milkings) causing false alarms was 1.1%. The model developed has the potential to be used as an on-farm decision aid and can be used in a real-time lameness monitoring system.

Key Words: hoof disease • expert system • lameness detection • automatic health monitoring

This article has been cited by other articles:

				N. Chapinal, A. M. de Passille, and J. Rushen Weight distribution and gait in dairy cattle are affected by milking and late pregnancy J Dairy Sci, February 1, 2009; 92(2): 581 - 588. [Abstract] [Full Text] [PDF]