In order to improve animal welfare and enhance the comfort of dairy cows, the application of information technology (IT) within the intensive livestock farming takes a key role in a proper routine management. 
This study aims to compare localisation and activity data provided by the CowView system, an automatic indoor localisation system for dairy cattle, with those obtained by a manual labelling procedure, twice within an observation period of minimum 25 hours per dataset. 
Data from five selected dairy cows were represented by behaviours performed in relation to the occupied zones, and were classified in two categories: activity and localisation.
The identified activities performed by the dairy cows were standing, walking (both considered as being in the alley), resting (being in the cubicle) and feeding (being at the feeder). Indeed, the zone considered in the analysis were alley, in bed and feeding zone. Data automatically and manually classified (used as a reference) were compared.
Among all the behaviours detected by the automated software, the most reliable results are those related to the activity of feeding (accuracy higher than 95%). The results showed that the CowView automatic monitoring system is able to identify activity zone classification (ALLEY, THROUGH, CUBICLES) with higher reliability compared to the specific activities performed by dairy cows. The results obtained  support the CowView system as an innovative and effective solution for an easier management of dairy cows

C. Bahr

D. Gottardo

E. Tullo

I. Fontana

K.H. Sloth

M. Guarino

S. Viazzi

AIR Universita degli studi di Milano

1 
 
Validation of a commercial system for the continuous and automated monitoring 1 
of dairy cows activity 2 
 3 
I. Fontana1, E. Tullo1, D. Gottardo1, C. Bahr2, S. Viazzi2, K. H. Sloth3, and M. Guarino1  4 
1Department of Veterinary and Technological Sciences for Food Safety, Faculty of 5 
Veterinary Medicine, Università degli Studi, Via Celoria 10, 20133 Milan, Italy  6 
2Department of Biosystems, Division M3-BIORES: Measure, Model & Manage Biore-7 
sponses, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium  8 
3GEA Farm Technologies GmbH Siemensstraße 25 59199 Bönen Germany  9 
emanuela.tullo@unimi.it 10 
 11 
Abstract  12 
 13 
In order to improve animal welfare and enhance the comfort of dairy cows, the 14 
application of information technology (IT) within the intensive livestock farming takes 15 
a key role in a proper routine management.  16 
This study aims to compare localisation and activity data provided by the CowView 17 
system, an automatic indoor localisation system for dairy cattle, with those obtained by 18 
a manual labelling procedure, twice within an observation period of minimum 25 hours 19 
per dataset.  20 
Data from five selected dairy cows were represented by behaviours performed in 21 
relation to the occupied zones, and were classified in two categories: activity and 22 
localisation. 23 
The identified activities performed by the dairy cows were standing, walking (both 24 
considered as being in the alley), resting (being in the cubicle) and feeding (being at the 25 
feeder). Indeed, the zone considered in the analysis were alley, in bed and feeding zone. 26 
Data automatically and manually classified (used as a reference) were compared. 27 
Among all the behaviours detected by the automated software, the most reliable results 28 
are those related to the activity of feeding (accuracy higher than 95%). The results 29 
showed that the CowView automatic monitoring system is able to identify activity zone 30 
classification (ALLEY, THROUGH, CUBICLES) with higher reliability compared to 31 
the specific activities performed by dairy cows. The results obtained  support the 32 
CowView system as an innovative and effective solution for an easier management of 33 
dairy cows.  34 
 35 
Keywords: behaviour, welfare, PLF, cow, herd monitoring 36 
 37 
Introduction 38 
 39 
Changes in the global demand of dairy products currently mean that the dairy industry is 40 
under pressure in increasing its productivity and efficiency (Gerber et al., 2011); 41 
moreover, the consolidation of farms has resulted in larger herd sizes (Von Keyserlingk 42 
et al., 2013), leading to difficulties for the farmer in identifying each individual cow and 43 
tracking its health and behaviour records. 44 
Monitoring the behaviour of dairy cows is useful to assess their welfare, health status 45 
and comfort at farm level (Mattachini et al., 2013). Indeed as reported by Huhtala et al. 46 
(2007) it has been seen that changes in cows' behaviour are strong indicators for their 47 
health and welfare problems and therefore they can be used as input to an early warning 48 
system. 49 
2 
 
Knowing the position of the cows is substantially important to monitor their behavioural 50 
patterns and activity (Huhtala et al., 2007) obtaining also information about the time 51 
spent in the different locations of the shed. In fact, the time spent by the cows lying or 52 
feeding plays an important role in terms of milk production (Fregonesi et al., 2007; 53 
Mattachini et al., 2011); therefore, continuous observation of those behaviours is a tool 54 
for the farmer to monitor and control cows' health status and production.  55 
However the continuous monitoring requires a lot of manpower/labour and it is time-56 
consuming (Fontana et al., 2014), for this reason Precision Livestock Farming (PLF) 57 
can combine information technology into on-line automated tools that can be used to 58 
control, monitor and model the behaviour of animals and their biological response 59 
(Tullo et al., 2013). 60 
Nowadays, the application of information technology (IT) within the intensive dairy 61 
farming takes a key role in a proper routine management in order to improve animal 62 
welfare and to enhance the comfort of dairy cows. 63 
Indeed, several studies confirmed the feasibility of the use of IT achieving excellent 64 
results in the identification and localisations of the animals, feeding patterns recognition 65 
and oestrus detection (Porto et al., 2014). 66 
 67 
The CowView system is an automatic indoor localisation system for dairy cattle 68 
providing positions and zone-related behavioural activities of tagged animals based on 69 
triangulation of very short radio-signals (Ultra Wide Band). The system is able to detect 70 
and monitor animal behavioural activities based on positioning, time at feeding table, 71 
time in bed, time standing and walking in the alley and distance travelled. 72 
This study aims to compare localisation and activity data provided by the CowView 73 
system with those obtained by a manual labelling procedure. The manual labelling of 74 
the video was used as Gold Standard for the comparison, in order to check the accuracy 75 
of the system in localising zone occupied by the cows and their activity. 76 
Therefore, data performed by five selected dairy cows were represented by behaviours 77 
in relation to the occupied zones, and were classified in two categories: activity and 78 
localisation. 79 
 80 
Materials and methods 81 
Data used for the comparison were divided in two datasets; the first one was used for 82 
the preliminary analysis of the output of the system, while the latter was obtained after 83 
the design optimisation of the CowView installation. The analysis consisted in the 84 
comparison between data collected automatically with the CowView system and the 85 
manual labelling performed on the video recordings obtained with a camera (Axis P5534 86 
PTZ Dome Network, 30 fps, and 1280x720 pixel) placed in top down perspective under 87 
the roof of the barn.  88 
Five selected cows (with a yellow letter on both flanks, and on the back of each cow) 89 
that were equipped with the CowView electronic tag were followed. 90 
Data used for the comparisons were represented by the zone-related activities performed 91 
by the selected dairy cows, and were classified in two categories: activity and 92 
localisation (Table 1). In both datasets, the “feeding” behaviour was considered when 93 
the cow’s head was in the fodder line. “In bed” behaviour was considered when at least 94 
two legs were in the cubicle, but the system was not able to recognise if the cows were 95 
lying or not. 96 
 97 
3 
 
Table1. Classification of data used for the analysis according to the behaviours 98 
performed by the cows or their localisation. 99 
Activity type Localisation of the cow 
Standing Alley 
Walking Alley 
In bed Cubicles 
Feeding Through 
At the drinker (only in the second dataset) At the drinker (only in the second dataset) 
 100 
First data set 101 
Data collection consisted in around 38 hours of video recordings divided in 5 days. 102 
Since, one hour a day (5 days) for five marked cows were considered, the dataset for the 103 
validation analysis included only 25 hours (90,000 seconds - 2.7x106 frames) of 104 
recordings. During the manual labelling procedure, each activity/localisation was 105 
classified recording the type, the zone and the duration. The resulting dataset was 106 
merged with the output of the CowView system, in order to obtain the true 107 
positives/negatives and the false positives/negatives. Data from manually labelled 108 
videos were used as reference value (Gold Standard). 109 
 110 
Second data set 111 
The video data set covered around 42 hours of labelled video (150,405 seconds - around 112 
4.5x106 frames) in 6 days. After the manual labelling, about 37 hours (132,053 seconds 113 
- around 3.9 x106 frames) of activity and localisation data were available. 114 
Also in this case, each activity/localisation was classified with the manual labelling 115 
procedure, recording the type, the zone and the duration.  116 
Only in this dataset the categories “at the drinker” were added to the cow 117 
activity/localisation classification. This activity was not classified as “drinking” since 118 
the CowView system could not identify this behaviour precisely . 119 
The resulting dataset was merged with the output of the CowView system, in order to 120 
obtain the true positives/negatives and the false positives/negatives. Data from manually 121 
labelled videos were used as reference values (Gold Standard). 122 
 123 
The parameters evaluated for each activity/localisation were: 124 
• Sensitivity, parameter that tests the true positive rate: 125 
Sensitivity = true positivestrue positive + false negative   
• Specificity, parameter that tests the true negative rate: 126 
Specificity =  true negatives
true negative + false positives
  
Predictive value for a positive result (PV+), parameter that tests the probability that the 127 
CowView detects behaviour that cow is actually performing: 128 
PV+=   true positive
true positive + false positive
 
Predictive value for a negative result (PV-): parameter that tests the probability that the 129 
CowView does not detect behaviour that cow is actually not performing: 130 
PV-= 
 true negatives
true negatives +  false negatives 
4 
 
Accuracy: parameter that expresses the proportion of correctly classified behaviours 131 
among all events detected 132 
Accuracy=
true positive  + true negative
all events
 
Results and discussion 133 
 134 
In Figure 1 an example of comparison between data manually labelled and 135 
automatically detected by the CowView system in relation to the activity of FEEDING 136 
is reported. Grey parts represent the amount of time (in seconds) spent by the selected 137 
cow in the activity of FEEDING detected by the CowView system. Black lines 138 
represent the actual time spent by that cow in the specific activity. Grey parts with the 139 
black frame represent the results obtained with the automated system that can be 140 
overlapped to the reference values (manual labelling). Black lines without grey filling 141 
represent a mismatch between the automated detection and the reference values. 142 
Figure1. Example of comparison between data manually labelled and automatically 143 
detected by the CowView system in relation to the activity of FEEDING on a selected 144 
Cow (A) and on a selected day. Values of “1” indicate the activity detection 145 
(manual/automated) 146 
 147 
In Tables 2 – 5 the results of the manual labelling and the continuous monitoring of the 148 
cow activity/localisation obtained with the CowView are reported for the first and the 149 
second dataset respectively. On the diagonal of the tables the acitivities/localisation that 150 
were detected both by the manual labelling and by the CowView System (true positive) 151 
are reported, while the rows represent the behaviours/localisation detected by the 152 
automated system that did not match with the reference values (manual labelling). 153 
All data are expressed in seconds. The last row is the total and actual amount of time 154 
spent by the five cows in a determinate activity/location, while the last column on the 155 
right represents the total amount of time that the cow spent in an activity /location 156 
according to the automated system. 157 
  158 
5 
 
Table 2. Results of the manual labelling and the continuous monitoring obtained on cow 159 
activity data with the CowView on the first dataset. 160 
    Manually labelled    
    STANDING WALKING IN BED FEEDING  Total 
CowView 
STANDING 11,851 171 686 323 13,031 
WALKING 2,300 2,322 671 2,488 7,781 
IN BED 17,542 137 22,870 76 40,625 
FEEDING 571 289 0 27,703 28,563 
    Total 32,264 2,919 24,227 30,590 90,000 
Table 3. Results of the manual labelling and the continuous monitoring obtained on cow 161 
localisation data with the CowView on the first dataset. 162 
    Manually labelled  
  
 
ALLEY IN BED FEEDING Total 
CowView 
ALLEY 16,305 1,623 2,776 20,704 
IN BED 2,799 37,812 76 40,687 
FEEDING 871 0 27,738 28,609 
Total 19,975 39,435 30,590 90,000 
Table 4. Results of the manual labelling and the continuous monitoring obtained on cow 163 
activity data with the CowView on the second dataset. 164 
 Manually labelled 
  STANDING WALKING CUBICLES  THROUGH  AT THE DRINKER Total 
CowView 
 
STANDING 13,125 237 1,014 798 947 16,121 
WALKING 3,075 4,801 243 697 267 9,083 
CUBICLES 889 214 44,336 0 1 45,440 
THROUGH 1,068 603 1,110 48,497 66 51,344 
AT THE DRINKER 2,687 316 0 0 7,062 10,065 
 Total 20,844 6,171 46,703 49,992 8,343 132053 
Table 5. Results of the manual labelling and the continuous monitoring obtained on cow 165 
localisation data with the CowView on the second dataset. 166 
    Manually labelled   
   ALLEY CUBICLES THROUGH AT THE DRINKER  
CowView 
ALLEY 21,147 1,207 1,457 1,151 24,962 
CUBICLES 1,132 44,386 0 1 45,519 
THROUGH 1,682 1,110 48,535 66 51,393 
AT THE DRINKER 3,054 0 0 7,125 10,179 
   27,015 46,703 49,992 8,343 132,053 
In Figures 2-8 the comparison between the sensitivity, specificity, PV+, PV- and the 167 
accuracy of the activity/location detected with the CowView system on the two data sets 168 
are displayed. After the design optimisation of the installation the five parameters 169 
considered (sensitivity, specificity, PV+, PV- and the accuracy) increased. In particular, 170 
the sensitivity for the STANDING activity and the specificity of the WALKING 171 
activity had nearly doubled. 172 
In general, there was an increase of accuracy for all the activities/locations that reached 173 
values between 92 and 97 %. 174 
The increase in sensitivity indicates an increase of the rate of true positives detected, 175 
meaning that the CowView system detected the same activity/location as the reference 176 
(manual labelling). The increase in specificity indicates an increase of the rate of true 177 
negative detected. In other words, the manual labelling and the CowView system can 178 
both detect if a behaviour is not occurring. 179 
6 
 
The increase in PV+ (Predictive value for a positive result) indicates the increased 180 
probability that the CowView detects a behaviour that the cow is actually performing. 181 
The increase in Predictive value for a negative result (PV-) indicates the increased 182 
probability that CowView does not detect behaviour that cow is actually not 183 
performing.  184 
Figure2. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 185 
the activity of STANDING detected with the CowView system on the two data sets. 186 
 187 
Figure3. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 188 
the activity WALKING detected with the CowView system on the two data sets. 189 
 190 
Figure4. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 191 
the activity IN BED detected with the CowView system on the two data sets.192 
 193 
  194 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV+ PV- ACCURACY 
STANDING 1st dataset STANDING 2nd dataset 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV+ PV- ACCURACY 
WALKING 1st dataset WALKING 2nd dataset 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV+ PV- ACCURACY 
IN BED 1st dataset IN BED 2nd dataset 
7 
 
Figure5. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 195 
the activity FEEDING detected with the CowView system on the two data sets. 196 
197 
Figure6. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 198 
the location ALLEY detected with the CowView system on the two data sets. 199 
 200 
Figure7. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 201 
the location CUBICLES detected with the CowView system on the two data sets. 202 
 203 
Figure8. Comparison between the sensitivity, specificity, PV+, PV- and the accuracy of 204 
the location THROUGH detected with the CowView system on the two data sets. 205 
 206 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV+ PV- ACCURACY 
FEEDING 1st dataset FEEDING 2nd dataset 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV + PV- ACCURACY 
ALLEY 1st dataset ALLEY 2nd dataset 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV + PV- ACCURACY 
CUBICLES 1st dataset CUBICLES 2nd dataset 
0% 
20% 
40% 
60% 
80% 
100% 
SENSITIVITY SPECIFICITY PV + PV- ACCURACY 
THROUGH  1st dataset THROUGH  2nd dataset 
8 
 
Conclusions 207 
The results of this comparison study showed that the CowView automatic monitoring 208 
system is able to identify activity zone classification (ALLEY, THROUGH, 209 
CUBICLES) with higher reliability compared to the zone –related activities performed 210 
by dairy cows. 211 
The preliminary results obtained are overall very encouraging even if the accuracy does 212 
not reach the 100%. 213 
Anyway, the software is an innovative solution and an extremely valuable tool for the 214 
management of large herds of dairy cattle. 215 
Further design optimisations of the CowView installation and relative validation will be 216 
necessary if the accuracy and the reliability of the system must be improved. 217 
 218 
Acknowledgements 219 
This project was funded by the European project no: 311825 EU-PLF (Animal and farm-220 
centric approach to precision livestock farming in Europe), co-financed by the European 221 
Commission. 222 
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Validation of a commercial system for the continuous and automated monitoring of dairy cows activity

https://air.unimi.it/retrieve/handle/2434/354816/522273/Validation%20of%20a%20commercial%20system%20for%20the%20continuous%20and%20automated%20monitoring%20of%20dairy%20cows%20activity_last.pdf

Validation of a commercial system for the continuous and automated monitoring of dairy cows activity

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