1
Ad ances in ho se mo phome ic measu emen s using 1 LiDAR 2 3
M. Pé ez-Ruiz1,2; D. Ta a -Ma ín1; M. J. Sánchez-Gue e o3; M. Vale a3 4
5
1. Uni e sidad de Se illa. Á ea de Ingenie ía Ag o o es al 6
Dp o. de Ingenie ía Ae oespacial y Mecánica de Fluidos, Spain. 7
2. Co esponding Au ho ’s con ac in o ma ion: 8
ETSIA. C a. Se illa-U e a km 1 9
Se illa, Spain 41013 10
Phone: 954 481 389 11
Fax: 954 486 436 12
E-mail: [email p o ec ed] 13
3. Uni e sidad de Se illa. Dp o. de Ciencias Ag o o es ales, Spain 14
15
16
Abs ac . 17
Zoome ic measu emen s ha e a po en ial alue in di e en ialing be ween indi iduals and wi hin 18
popula ions. The measu emen o body size in ho ses and li es ock plays a signi ican ole in 19
unc ional longe i y, p oduc ion, and ep oduc i e pe o mance and heal h. In his con ex , he 20
measu emen s ob ained wi hou con ac by de ec ion sys ems and isualized by compu e s 21
could ep esen a g ea ad ance o e con en ional measu emen s ha a e edious, ime 22
consuming and s ess ul o he animals. This s udy p esen s a new app oach o aking 23
zoome ic measu emen s o an animal's body based on digi al h ee-dimensional modelling. The 24
cap u e o he da a se ies was ca ied ou by a LiDAR senso . The 16 lase beams o he senso 25
we e able o ully scan a ho se, pe o ming a 3D econs uc ion o he ho se's side, h ough 26
which body measu emen s we e ob ained. Fi e Pu a Raza Española ho ses (PRE) (3 s allions 27
and 2 ma es) wi h ages anging be ween 5 and 18 yea s old we e scanned. The PRE is he 28
mos ecognized na i e Spanish ho se popula ion o i s census (na ional and in e na ional), 29
2
cul u al and socioeconomic impo ance. Fo each ho se, 17 zoome ic measu emen s (linea 30
and angula ) we e aken bo h manually and using he LiDAR-based sys em o check he 31
use ullness o his non-in asi e echnology in ob aining quick li es ock measu emen s while 32
causing minimal s ess o he animals. O he 17 zoome ic measu emen s ob ained manually 33
and wi h he senso , 10 (58.82%) had a mean ela i e e o ha anged be ween > 0 and <10; 5 34
(29.41%) had an e o ha anged ≥10 and <20; and wo (As and AC ) had an e o ≥20 35
(11.76%). A o al o 82.5% o he ai s s udied had an accu acy ( 2) lowe han 5%. The e o e, 36
al hough his app oach could s ill be imp o ed, i e i ies he iabili y o noncon ac 37
measu emen s o la ge li es ock. 38
39
Keywo ds. Zoome ic measu emen s, digi al h ee-dimensional modelling, LiDAR senso , 40
Velodyne VLP-16 41
1. In oduc ion 42
The use o emo e sensing in ag icul u e is no new; i da es back decades. Howe e , 43
ecen echnological ad ances in senso s, ins umen a ion, and wi eless ne wo ks ha e b ough 44
inno a i e le els o moni o ing in o aising li es ock (Guo e al., 2017, 2019; Le Cozle e al., 45
2019). Senso s measu ing animal wel a e, heal h, g ow h a e, and physiological ea u es, 46
including shape, size, and weigh , ei he alone o in combina ions, ha e been applied in animal 47
b eeding. Kawasue e al. (2013) used h ee Kinec senso s o in es iga e he weigh and size o 48
ca le, as well as hei pos u es and shapes, wi h accu acies o up o 93% ela i e o manual 49
measu emen s. O he au ho s ha e also e alua ed he body shape and empe a u e o black 50
ca le using a he mal came a and a Kinec senso (Kawasue e al., 2017), he co ela ion 51
be ween manual measu emen s and s e eo ision in ho ses (Pallo ino e al., 2015), and he 52
3
combina ion o a binocula s e eo ision sys em wi h he LabView de elopmen pla o m o 53
es ima e pig body size and weigh in indoo a m condi ions (Shi e al., 2016). 54
The ho se indus y plays a pa in na ional, s a e, and local economies in Eu ope, No h 55
Ame ica, and Canada (C oss, 2019). The equine sec o is di e se, in ol ing ag icul u e, 56
business, ec ea ion, acing, compe i ions, and he gene a ion o specialized skills and gene al 57
employmen ac oss he boa d. B eeding, compe i ion, and leisu e ac i i ies in ol ing ho ses a e 58
impo an business conce ns, and while ho ses a e no longe used o p ima y anspo , hey 59
emain impo an asse s. Al hough no cu en ly popula in mos coun ies, he consump ion o 60
ho se mea is inc easing in se e al Wes e n Eu opean coun ies due o i s a ailabili y and 61
ecognized nu i ional alue and mainly as an al e na i e o he adi ional consump ion o ed 62
mea (Belaunza an e al., 2015). 63
Equine p oduc ion is one o he main ag icul u al ac i i ies in Spain, esponsible o 0.5% o 64
he g oss na ional p oduc (Sánchez-Gue e o e al., 2018), wi h he Pu a Raza Española ho se 65
(PRE) being he mos impo an b eed in e ms o his o ical census (328,706 ho ses in 65 66
coun ies) and economic impac on in e na ional ade (Sánchez-Gue e o e al., 2016). Typical 67
gene ic selec ion p og ammes a e based on unc ionali y (d essage) and mo phological 68
cha ac e is ics. The e o e, ob aining accu a e zoome ic measu emen s, cha ac e ized by high 69
epea abili y and low a iance among obse e s, can be conside ed ideal condi ions in he 70
con ex o an animal b eeding p og amme (Duensign e al., 2014). In he case o ho ses, he 71
close connec ion be ween biome ic pa ame e s, locomo ion cha ac e is ics, and spo s 72
pe o mance is o en he only ool o gene ic imp o emen (Pallo ino e al., 2015) and as an 73
indica o o unc ional longe i y. In ecen decades, indi ec con o ma ion assessmen sys ems 74
based on linea mo phological ai s ha e been implemen ed o many equine b eeds (Kuhnke 75
e al., 2019; Folla e al., 2019). This sys em has a se ies o ad an ages, as he sco ing scale o 76
each egion co e s i s biological ange and a wide ange o nume ical classes can be used, 77
which allows subsequen s a is ical ea men o he da a in a con inuous scale. Howe e , highly 78
4
ained quali ie s a e equi ed o apply he linea mo phological sys em, and he ela ionship 79
be ween zoome ic measu emen s and he linea scale used has no been es ablished. The 80
main goal o he PRE b eeding p og amme is o imp o e no only animal unc ionali y bu also 81
i s con o ma ion o spo pe o mance (Sánchez-Gue e o e al., 2016). The e o e, in he PRE, 82
he impo ance o con o ma ion has been shown in s udies ca ied ou o demons a e i s 83
ela ionship wi h d essage (Sánchez-Gue e o e al., 2017; Solé e al., 2013), unc ional 84
longe i y (Solé e al., 2017), and gene ic imp o emen o mo pho unc ional ai s (Sánchez-85
Gue e o e al., 2017). 86
In he las decade, se e al compu e ized echniques ha e been p oposed o he collec ion 87
o biome ic da a h ough images (Wu e al., 2004; Viazzi e al., 2014). No el h ee-dimensional 88
sys ems can sol e he p oblems posed by con en ional wo-dimensional ision sys ems, 89
including s e eo pho og amme ic echniques. Howe e , hese pho og amme ic sys ems a e 90
di icul o implemen . 91
In ecen yea s, new echniques ha p o ide poin clouds in a as , non-in asi e and 92
inexpensi e way, such as he Kinec 2® (Mic oso Co p., Redmond, Washing on) o X ion P o 93
(USUSTeK COMPUTER Inc., Taipei, Taiwan), as well as in a ed (IR) ligh (Salau e al., 2017; 94
Kawasue e al., 2017; Viazzi e al., 2014; Mo ensen e al., 2016; Guo e al., 2017; Pezzuolo e 95
al., 2018; Song e al., 2018; Guo e al., 2019), a e being used o de elop new me hod o 96
au oma e he collec ion o mo phological in o ma ion om li es ock. In ac , Pezzuolo e al. 97
(2019) pe o med a me ological analysis o he S uc u e om Mo ion (S M) app oach, low-cos 98
LiDAR scanning, and he Mic oso Kinec 1 dep h came a o h ee-dimensional measu emen 99
o an animal's body, wi h speci ic e e ence o pigs. The esul s ob ained demons a ed he high 100
po en ial o he 3D Kinec and a highe oo mean squa e (RMS) alue o LiDAR wi h espec o 101
Kinec and S M, p obably due o he collec ion app oach based on indi idual p o iles ins ead o 102
su aces. 103
5
Howe e , despi e p og ess in he h ee-dimensional econs uc ion o he bodies o animals, 104
no obus desc ip o o he au oma ic es ima ion o con o ma ion has cu en ly been ound in 105
equine species. The e o e, he main objec i e o his s udy is o explo e a 3D econs uc ion 106
app oach o body measu emen s in ho ses using poin cloud analysis. Ou speci ic objec i es 107
we e (1) o de elop a po able scanning sys em o domes ica ed animals; (2) o ob ain aluable 108
zoo echnical in o ma ion h ough 3D poin cloud analysis ( ha would a oiding was ing ime and 109
money); and (3) o compa e he accu acy be ween he digi al and con en ional manual 110
measu emen s on i e PRE ho ses. 111
112
2. Ma e ials and me hods 113
2.1. Animals and mo phological a iables unde s udy 114
Fi e PRE ho ses, belonging o he b eeding p og amme o he “Yeguada Ca ujana del 115
Hie o del Bocado” (Je ez de la F on e a, Cádiz, Spain), si ua ed a la i ude 36°41′ no h and 116
longi ude 06°09′ wes , we e analysed (Table 1). 117
Table 1. Ages and coa colou s o he i e Pu a Raza Española ho ses used in his s udy. 118
S allion 1
S allion 2
S allion 3
Ma e 1
Ma e 2
Age
5 yea s old
6 yea s old
8 yea s old
12 yea s old
18 yea s old
Coa Colou
G ey
Bay
G ey
G ey
G ey
119
The animals had been used o b eeding and d essage exhibi ions. To educe possible 120
a ia ions, he zoome ic measu emen s aken by he manual me hod we e always ca ied ou 121
by he same e e ina ian. Fo each ho se, 17 zoome ic measu emen s (15 linea and 2 122
angula ) ela ing o spo pe o mance (Sánchez-Gue e o e al., 2016, 2017) we e collec ed 123
(Fig. 1). 124

6
Figu e. 1. G aphical ep esen a ion o he mo phological measu emen s aken in he Pu a
Raza Espanola s allion 1 o he Yeguada Ca ujana del Hie o del Bocado. Heigh a he
wi he s (HW); Heigh o he lowes poin a he wi he s (HWl); Heigh a he c oup (HC );
Leng h o he body (LB); Leng h o he head (LH); Wid h o he head (WH); Leng h o he neck
(LN); Leng h o he shoulde (LS); Leng h o he o ea m (LFA); Do so-s e num diame e
(DSD); Leng h o he c oup (LC ); Leng h o he emu (LF); Dep h o he c oup (DC ); Leng h
o he gaskin (LG); Leng h o he bu ock (LBu); Angle o he shoulde (AS); Angle o he
c oup (AC ).
125
The mo phological measu emen s aken we e as ollows: 126
1. Heigh a he wi he s (HW): measu ed om he g ound o he highes poin o he 127
wi he s. 128
2. Heigh o he lowes poin a he wi he s (HWl): measu ed om he g ound o he lowes 129
poin o he wi he s. 130
3. Heigh a he c oup (HC ): measu ed om he g ound o he highes poin o he ube 131
coxae. 132
4. Leng h o he body (LB): measu ed be ween he ube cle o he hume us and he ischial 133
ube osi y. 134
7
5. Leng h o he head (LH): dis ance om he nape o he c anial bo de o he snou . 135
6. Wid h o he head (WH): dis ance be ween he mos p o uding edge o he zygoma ic 136
a ches. 137
7. Leng h o he neck (LN): dis ance be ween he base o he ea and he middle poin o 138
he spine o he scapula. 139
8. Leng h o he scapula (LS): dis ance be ween he wi he and shoulde . 140
9. Leng h o he o ea m (LFA): dis ance be ween he pa allel s aigh lines d awn down 141
om he elbow and ca pal join midpoin . 142
10. Do so-s e num diame e (DSD): dis ance measu ed om he lowes poin in he wi he 143
decline o he s e nal a ea. 144
11. Leng h o he c oup (LC ): dis ance be ween he coxal ube osi y a i s midpoin and he 145
ischial ube osi y. 146
12. Leng h o he emu (LF): dis ance be ween he bu ock and s i le 147
13. Dep h o he c oup (DC ): dis ance be ween he hip and s i le. 148
14. Leng h o he gaskin (LG): dis ance be ween he s i le and he hock 149
15. Leng h o he bu ock (LBu): dis ance be ween he coxal ube osi y o he ilium and he 150
ischial ube osi y. 151
16. Angle o he shoulde (AS): angle o med by he line om he wi he s o he shoulde 152
wi h he ho izon al. 153
17. Angle o he c oup (AC ): angle o med by he line om he ischial ube osi y o he 154
ube coxae wi h he ho izon al. 155
156
2.2. Da a acquisi ion and da a p ocessing 157
2.2.1 Con en ional measu emen sys em 158
8
Fo each ho se, zoome ic measu emen s we e sys ema ically collec ed using s anda d 159
measu ing s icks, non-elas ic measu ing ape and zoome ic compasses (Sánchez-Gue e o e 160
al., 2016). All measu emen s we e aken om he le side o he ho se while i was s anding on 161
a ha d su ace and la g ound, assuming a na u al posi ion. The ho ses we e posi ioned o 162
measu emen wi h he on legs and hind ee pa allel and as nea o pe pendicula as possible; 163
he oes we e in line. No seda i es we e used. 164
165
2.2.2. Measu emen s based on he poin cloud sys em 166
The zoome ic measu emen s we e acqui ed h ough an op ical emo e sensing echnique 167
using a LiDAR senso . The senso uses lase ligh o ob ain a dense sampling o he a ge , 168
p oducing accu a e measu emen s in h ee dimensions. The e lec ion o lase ligh o he a ge 169
is de ec ed and analysed by he ecei e s in he LiDAR senso . These ecei e s, consis ing o a 170
ecei e elescope, il e s, and an op ical de ec o , eco d he p ecise ime be ween when he 171
lase pulse le he sys em and when i e u ned o calcula e he limi dis ance be ween he 172
senso and he a ge . These spa ially o ganized pos -p ocessed LiDAR da a a e known as poin 173
cloud da a, which is a la ge da a se consis ing o 3D poin da a. Each LiDAR poin can ha e an 174
assigned classi ica ion de ining he ype o objec ha e lec ed he lase pulse. 175
The LiDAR used was a Velodyne VLP-16 (Velodyne Lida , Inc., Cali o nia, EEUU), which is 176
a high-p ecision 3D lase senso LiDAR wi h a o a ing head con aining a ce ain numbe o 177
semiconduc o lase s o lase diodes. Each o he lase s has i s own de ec o (Fig. 2). 178
9
Figu e 2. Rep esen a ion o he lase s o he Velodyne VLP-16
179
The VLP-16 senso measu es he e lec i i y o an objec wi h 256-bi esolu ion 180
independen o lase powe and dis ance wi hin an in e al o 1-100 m. Comme cially a ailable 181
e lec i i y s anda ds we e used o absolu e e lec i i y calib a ion, which a e s o ed in a 182
calib a ion able wi hin he ield-p og ammable ga e a ay (FPGA) o he VLP-16. 183
The VLP-16 scanne has 16 indi idual lase s/de ec o s a anged in a 30° FOV, which yields 184
a e ical esolu ion o 2.0°. This senso has an FOV symme ical wi h he espec o he 185
ho izon al plane, and poin s can be ob ained up o 100 m away a a a e o app oxima ely 186
300,000 pe second in single e u n and 600,000 o dual e u n. The ho izon al FOV is 360°, 187
wi h an adjus able o a ion equency be ween 5 and 20 Hz. 188
Thanks o he di e gence o he lase beam, a single sho can hi mul iple objec s, and 189
di e en e u ns will occu . The VLP-16 has he abili y o analsze he mul iple e u ns and epo 190
he s onges e u n, he las e u n, o bo h (dual e u n o dual mode). Mul iple e u ns occu 191
when a lase pulse s ikes he ho se in a loca ion ha does no comple ely block he pa h o he 192
pulse, allowing he emaining po ion o he pulse o con inue o he nex seen objec . 193
Communica ion be ween he senso and he compu e pe o ming he da a analysis was 194
pe o med was ca ied ou h ough an in e ace box wi h an E he ne cables and use -assigned 195
IP add esses. 196
16
Table 2. Zoome ic measu emen s, ela i e e o , 2 e o and Pea son co ela ions in i e Pu a Raza Española ho ses om Es epe Ca ujana 286 ob ained using he con en ional p ocedu e (manual) and he LiDAR sys em (senso ) 287
M
S allions 1
S allions 2
S allions 3
Ma e 1
Ma e 2
Global
2
m s .e. m s .e. m s .e. m s .e. m s .e.
HW
156
160
-2.56
159
149
6.29
164
156
4.88
160
149
6.88
163
150
7.98
1.14
-0.32
HWl
151
141
6.62
150
148
1.33
153
155
-1.31
149
146
2.01
153
150
1.96
3.59
0.60
HC
158
145
8.23
159
155
2.52
159
140
11.95
160
154
3.75
162
161
0.62
23.17
0.77
LB
156
150
3.85
155
155
0.00
159
153
3.77
159
158
0.63
170
156
8.24
0.86
0.39
LH
60
63
-5.00
58
57
1.72
59
62
-5.08
61
57
6.56
62
60
3.23
0.52
0.06
WH
23
29
-26.09
23
22
4.35
24
23
4.17
21
17
19.05
23
25
-8.70
3.92
0.64
LN
74
78
-5.41
73
70
4.11
79
73
7.59
78
78
0.00
78
75
3.85
0.64
0.25
LS
62
53
14.52
62
54
12.9
62
67
-8,06
66
73
10.61
69
70
-1.45
0.98
0.71
LFA
54
54
0.00
47
56
19.15
49
40
18.37
44
37
15,91
47
51
8.51
1.07
0.48
DSD
72
54
25.00
71
54
23.94
74
62
16.22
74
66
10.81
77
67
12.99
3.58
0.90
LC
56
50
10.71
54
54
0.00
52
51
1.92
52
54
3.85
53
55
3.77
1.86
-0.45
LF
53
44
16.98
50
42
16.00
51
48
5.88
48
41
14.58
45
42
6.67
0.86
0.53
DCR
52
48
7.69
49
39
20.41
52
53
1.92
52
35
32.69
53
49
7.55
17.70
0.47
LG
51
64
25.49
48
49
2.08
50
50
0
57
62
8.77
54
55
1.85
2.32
0.62
LBu
47
45
4.26
45
41
8.89
48
47
2.08
42
39
7.14
45
47
4.44
0.25
0.79
AS
57
66
15.79
57
86
50.88
56
62
10.71
58
83
43.1
57
65
14.04
230.60
0.66
AC
14
25
78.57
18
10
44.44
18
23
27.78
17
21
23.53
18
20
11.11
4.17
-0.55
Measu emen s (M); con en ional p ocedu e (m) and he LiDAR sys em (s); ela i e e o ( .e.); Pea son co ela ions coe icien ( ); MHeigh a wi he s1 (HW); 288 Heigh a c oup1 (HC ); Leng h o head1 (LH); Wid h o head1 (WH); Leng h o neck1 (LN); Leng h o shoulde 1 (LS); Leng h o o ea m1 (LFA); Leng h o body1 289 (LB); Leng h o c oup1 (LC ); Leng h o emu 1 (LF); Leng h o gaskin1 (LG); Dep h o c oup1 (DC ); Leng h o bu ock1 (LBu); Do so-s e num diame e 1 (DSD); 290 Angle o shoulde 2 (AS); Angle o c oup2 (AC ). 1Cen im e s 2Deg ees 291

17
Size and body con o ma ion a e c i ically impo an ai s in nea ly all ho ses b eeds, which 292
a e p esumably subjec o a s ic p ocess o selec ion o e ime (B ooks e al., 2010). The 293
zoome ic measu emen s eco ded du ing his s udy we e simila o hose ob ained p e iously in 294
he same b eed (Molina e al., 1999; Gómez e al., 2009). Al hough he bay PRE ho ses a e he 295
alles in his s udy, he heigh o s allion 2 was be ween ha o s allions 1 and 3. 296
The measu emen s we e aken bo h in ma es and s allions o measu e he possible 297
di e ence in accu acy o he measu emen a he ho se's mane; in ma es, he mane is usually 298
cu , and in s allions i is usually le long (Fig. 1 and 4). In ou esul s, s allions had a highe 299
ela i e e o han ma es (10.83 and 8.80, espec i ely) and a highe a e age 2 alue. 300
Among he h ee s allions, he ela i e e o anged be ween 0 (HC, LFA, LC and LC) and 301
78.57 (Ac ). A o al o 7.84% o he zoome ic measu emen s s udied had a ela i e e o o 0; 302
o 52.94% had an e o > 0 and <10; o 21.57% had an e o ≥10 and <20; o 11.76% had an 303
e o ≥20 and <30; and o 5.88% had an e o ≥ 30. Fo he wo ma es s udied, he ela i e 304
e o anged be ween 0 (LN) and 43.10 (AS). A o al o 2.94% o he zoome ic measu emen s 305
s udied had a ela i e e o o 0; o 64.71% had an e o > 0 and <10; o 23.53% had an e o 306
≥10 and <20; o 2.94% had an e o ≥20 and <30; and o 5.88% had an e o ≥ 30 (Table 2). 307
The mean ela i e e o was 10.83% and 8.00% wi hou angle measu emen s. A o al o 82.5% 308
o he s udied ea u es had an accu acy ( 2) o less han 5%; only one o he angle 309
meau emen s (AS) had a highe 2 alue. The Pea son co ela ion be ween he manual and 310
senso measu emen s anged om 0.90 (DSD) o 311
-0.55 (Ac ) o -0.45 (LC ) i he angles we e no aken in o accoun (Table 3). The angles a e, in 312
gene al, e y di icul o measu e also in he adi ional sys em and in he linea mo phological 313
assesmen s since hey a e in luenced by he ho se's pos u e (Sánchez e al., 2013). We 314
sugges ha pe haps his p oblem could be sol ed wi h he use o LiDAR o a simila echnique, 315
since he scan could be pe o med when he animal is pe ec ly poised, and in eal li e, he 316
ho se is always mo ing o some deg ee while zoome ic measu emen s a e being aken. The R² 317
18
and RMSE es ima ed by compa ing he zoome ic measu emen s ob ained using he 318
con en ional p ocedu e (manual) and he LiDAR sys em (senso ) we e 0.97 and 7.62, 319
espec i ely (Figu e 8). 320
y = 2.4251 + 0.94268 * x
= 0.9861; R
2
=0.9723; RMSE=7.6172
020 40 60 80 100 120 140 160 180
Manual zoome ic measu emen
0
20
40
60
80
100
120
140
160
180
LIDAR zoome ic measu emen
0.95 Con idence In e al
321 Figu e 8. A sca e plo wi h eg ession analysis be ween he coe icien o de e mina ion (R²) and oo -322 mean-squa e e o (RMSE) compa ing zoome ic measu emen s ob ained o i e Pu a Raza Española 323 ho ses om Es epe Ca ujana pe o med using he con en ional p ocedu e (manual) and he LiDAR 324 sys em (senso ) 325
326
Thus, ou esul s di e om hose epo ed o di e en body measu es on Medi e anean 327
bu aloes (Neg e i e al., 2007), co ela ing adi ional measu es wi h p edic ed ones using 328
image analysis and ob aining coe icien s anging be ween 0.91 and 0.99 depending on he 329
measu ed ai . Mo eo e , Pallo ino e al. (2015) co ela ed adi ional measu emen s wi h hose 330
ob ained wi h a dual web-came a sys em ( = 0.998) in Lipizzan ho ses. In any case, I is 331
19
impo an o highligh ha he Pea son co ela ion coe icien s a e no he bes way o de ec he 332
di e ences be ween wo me hods o measu emen , since we a e no as in e es ed in whe he 333
hey inc ease o dec ease in he same di ec ion. The main in e es is o ensu e ha he 334
di e ence be ween he wo measu es is as small as possible. This could be be e es ima ed 335
wi h he ela i e e o . Fo he body size measu emen s o sheep, a low-cos dual web came a 336
was used, yielding a mean ela i e e o o 5% (Menesa i e al., 2014). In addi ion, 16 adul 337
dai y cows we e s udied wi h pai s o pic u es, aken wi h a po able ins umen inco po a ing 338
wo synch onized came as, wi h pho og amme ic measu emen s ha ing an accu acy wi hin 0.5 339
cm (Gaudioso e al., 2014). The use o a s uc u ed ligh dep h came a o h ee-dimensional 340
body measu emen s o dai y cows in ee-s all ba ns has also been e alua ed (Pezzuolo e al., 341
2018), ob aining coe icien s o de e mina ion o R2 > 0.84 and de ia ions lowe han 6% wi h 342
espec o manual measu emen s. As in ou case, lowe pe o mances we e ob ained o he 343
angle measu emen s (back slope: R2 = 0.12). In ou s udy, he AS measu emen had a e y 344
high 2 alue, while AC had a low 2 alue in bo h gende s. This is he i s s udy in which 17 345
zoome ic measu emen s we e analysed, wi h he majo i y o he p e ious s udies ha ing ewe 346
ai s analysed ( anging om 3 o 8 body measu emen s (Huang e al., 2018; Pezzuolo e al., 347
2018)). Taking accu a e manual measu emen da a as alida ion c i e ia, LiDAR me hodology 348
was used in ano he s udy o measu e h ee li e ca le, and he expe imen al esul s showed 349
ha he inal de ia ions we e close o 2 mm and wi hin app oxima ely 2% (Huang e al., 2018). 350
The ep oducibili y o hese ai s in he linea mo phological sys em anged om 0.86 o 0.99 in 351
PRE (Sánchez e al., 2013). In s udying ano he g oup o zoome ic measu emen s, Gaudioso 352
e al. (2014) ound a s anda d de ia ion o 2.0 cm. I shold be no ed ha we ha e no been able 353
o s udy he p ecision o hese 17 zoomome ic measu emen s since eplica ions we e no 354
ca ied ou . 355
Once he me hodology p oposed in his p ojec has been p esen ed and he esul s 356
ob ained om his p ocess ha e been analysed, he inal de elopmen o wo conclusions can 357
20
be p esen ed, one om a gene ic poin o iew and ano he om a speci ic poin o iew, in 358
addi ion o a oadmap o u u e de elopmen s. 359
4. Conclusions 360
A gene al me hodology has been c ea ed ha es ablishes a da a e alua ion p ocess, so ha in 361
he u u e, applica ions can be de eloped in which we can encompass he en i e p ocess. The 362
use o a LiDAR sys em has simpli ied he aking o measu emen s, gi ing ise o a me hod ha 363
can ob ain op imal esul s in moni o ing he g ow h o he li es ock. This me hodology speci ies 364
a clea , p ecise and logical p ocess. 365
In heo y, his me hod can be ex apola ed o any equine b eed, as well as o o he li es ock 366
species. Al hough i is a pilo s udy and i s accu acy mus be imp o ed, i could help in 367
p e en ing excess spending and s essing animals wi h he cu en manual zoome ic 368
measu emen . Mo e speci icically, he ollowing conclusions can be d awn based on he esul s 369
ob ained om implemen ing he me hodology. The app oach ca ied ou in his wo k e i ies he 370
iabili y o he noncon ac measu emen o la ge li es ock. Knowledge o he scalable g ow h 371
and animal wel a e a he loca ion whe e he animals a e housed will imp o e he quali y o he 372
animals and gene ic b eeding. Howe e , as can be seen in he esul s sec ion, due o he 373
cons an mo emen o he ho ses and he limi a ion o single- ame analysis, i is no possible o 374
ully analyse he h ee-dimensional image o he ho se. The geome ic accu acy o hese 375
senso s is closely ela ed o he quali y o hei ex e nal o ien a ion. This ex e nal o ien a ion is 376
wha we will ob ain om he in eg a ed na iga ion sys ems (GNSS and INS). The lase scanne 377
measu es only he ec o o ien ed om he opening o he lase sys em o an objec poin 378
no mally on he g ound, he ho se. Th ee-dimensional poin s can only be calcula ed i a any 379
momen he posi ion and o ien a ion o he LiDAR senso wi h espec o a coo dina e sys em is 380
known. A u u e s udy will y o imp o e hese d awbacks by in eg a ing wo synch onized poin 381
21
clouds h ough wo LiDARs posi ioned on each side o he animal and pe o ming epea ed 382
measu emen s wi h he con en ional p ocedu e (manual) and he LiDAR sys em (senso ). 383
Fo his p oblem, in he nea u u e his sys em will con inue o be op imized by ac i a ing he 384
LiDAR senso and an in eg a ed na iga ion sys em ha will allow he senso o mo e so ha he 385
ho se can be o a ed while i is being scanned. This will u he allow a comple e econs uc ion 386
o he ho se in 3D o g ea e accu acy in ob aining he zoome ic a iables. 387
388
Acknowledgemen s 389
The esea ch was suppo ed by p ojec AGL2016-78964-R unded by he Spanish Minis y o 390
Economics and Compe ence. Addi ionally, he au ho s wan o hank he Yeguada Ca ujana-391
Hie o del Bocado in Je ez (Cádiz) o p o iding us wi h hei acili ies and access o hei 392
animals. 393
Re e ences 394
Belaunza an, X., Bessa, R.J.B., La ín, P., Man ecón, A.R., K ame , J.K.G., Aldai, N., 2015. 395 Ho se-mea o human consump ion - Cu en esea ch and u u e oppo uni ies (Re iew). 396 Mea Science. 108, pp. 74-81. h ps://doi.o g/10.1016/j.mea sci.2015.05.006 397
B ooks, S.A., Mak andi-Nejad, S., Chu, E., Allen, J.J., S ee e , C., Gu, E., McClee y, B., 398 Mu phy, B.A., Bellone, R., Su e , N.B., 2010. Mo phological a ia ion in he ho se: 399 de ining complex ai s o body size and shape. Animal Gene ics. 41(2), pp.159–65. 400 h ps://doi.o g/10.1111/j.1365-2052.2010.02127.x 401
C oss, P., 2019. Global Ho se s a is ics in e nal. Global Ho se s a is ics. HPAB (HiPoin Ag o 402 Bedding Co p.) Helping he Plane h ough Animal Biodi e si y. Global Ho se. 403
Duensing, J., S ock, K.F., K ie e , J., 2014. Implemen a ion and p ospec s o linea p o iling in 404 he wa mblood ho se. Jou nal o Equine Ve e ina y Science. 34, pp. 360–368. 405 h ps://doi.o g/10.1016/j.je s.2013.09.002 406
Folla, F., Sa o i, C., Guzzo, N., Pigozzi, G., Man o ani, R., 2019. Gene ics o linea ype ai s 407 sco ed on young oals belonging o he I alian Hea y D augh Ho se b eed. Li es ock 408 Science. 219, pp. 91–96. h ps://doi.o g/10.1016/j.li sci.2018.11.019 409
Gaudioso, V., Sanz-Ablanedo, E., Lomillos, J.M., Alonso, E., Ja a es-Mo illo, L., Rod íguez, P., 410 2014. “Pho ozoome e ”: A new pho og amme ic sys em o ob aining mo phome ic 411

22
measu emen s o elusi e animals. Li es ock Science.165, pp 147–156. 412 h ps://doi.o g/10.1016/j.li sci.2014.03.028 413
Gómez, M.D., Goyache, F., Molina, A., Vale a, M., 2009. Si e × s ud in e ac ion o body 414 measu emen ai s in Spanish Pu eb ed ho ses. Jou nal o Animal Science. 87, pp. 2502–415 2509. h ps://doi.o g/10.2527/jas.2008-0841 416
Guo, H., Ma, X., Ma, Q., Wang, K., Su, W., Zhu, D., 2017. LSSA_CAU: An in e ac i e 3d poin 417 clouds analysis so wa e o body measu emen o li es ock wi h simila o ms o cows o 418 pigs. Compu e s and Elec onics in Ag icul u e. 138, pp. 60–68. 419
h ps://doi.o g/10.1016/j.compag.2017.04.014 420
Guo, H., Bo Li, Z., Ma, Q., Zhu, D., Su, W., Wang, K., Ma inello, F., 2019. A bila e al symme y 421 based pose no maliza ion amewo k applied o li es ock body measu emen in poin 422 clouds. Compu e s and Elec onics in Ag icul u e. 160, pp. 59-70. h ps://doi.o g/ 423 10.1016/j.compag.2019.03.010 424
Huang, L., Li, S., Zhu, A., Fan, X., Zhang, C., Wang, H., 2018. Non-con ac body measu emen 425 o qinchuan ca le wi h LiDAR senso . Senso s. 18(9), pp. 3014. 426 h ps://doi.o g/10.3390/s18093014 427
Kawasue, K., Ikeda, T., Tokunaga, T., Ha ada, H., 2013. Th ee-dimensional shape 428 measu emen sys em o black ca le using KINECT senso . In e na ional Jou nal o 429 Ci cui s, Sys em and Signal P ocessing. 7, pp. 222–230. 430
Kawasue, K., Win, K.D., Yoshida, K., Tokunaga, T., 2017. Black ca le body shape and 431 empe a u e measu emen using he mog aphy and KINECT senso . A i icial Li e and 432 Robo ics. 22, pp. 464–470. h ps://doi.o g/10.1007/s10015-017-0373-2 433
Kuhnke, S., Bä , K., Bosch, P., Rensing, M., Bo s el, U.K.V., 2019. E alua ion o a No el 434 Sys em o Linea Con o ma ion, Gai , and Pe sonali y T ai Sco ing and Au oma ic 435 Ranking o Ho ses a B eed Shows: A Pilo S udy in Ame ican Qua e Ho ses. Jou nal o 436 Equine Ve e ina y Science. 78, pp. 53–59. h ps://doi.o g/10.1016/j.je s.2019.04.002 437
Le Cozle , Y., Allain, C., Caillo , A., Deloua d, J.M., Dela e, L., Luginbuhl, T., Fa e din, P., 438 2019. High-p ecision scanning sys em o comple e 3D cow body shape imaging and 439 analysis o mo phological ai s. Compu e s and Elec onics in Ag icul u e. 157, pp. 447–440 453. h ps://doi.o g/10.1016/j.compag.2019.01.019 441
Menesa i, P., Cos a, C., An onucci, F., S e i, R., Pallo ino, F., Ca illo, G., 2014. A low-cos 442 s e eo ision sys em o es ima e size and weigh o li e sheep. Compu e s and Elec onics 443 in Ag icul u e. 103, pp. 33–38. h ps://doi.o g/10.1016/j.compag.2014.01.018 444
Molina, A., Vale a, M., Dos San os, R., Rode o, A., 1999. Gene ic pa ame e s o 445 mo pho unc ional ai s in Andalusian ho se. Li es ock P oduc ion Science. 60, pp. 295–446 303. h ps://doi.o g/10.1016/S0301-6226(99)00101-3 447
Mo ensen, A.K., Lisouski, P., Ah end , P., 2016. Weigh p edic ion o b oile chickens using 3d 448 compu e ision. Compu e s and Elec onics in Ag iculu e. 123, pp. 319–326. 449 h ps://doi.o g/10.1016/j.compag.2016.03.011 450
Neg e i, P., Bianconi, G., Ba occi, S., Te amoccia, S., Ve na, M., 2008. De e mina ion o li e 451 weigh and body condi ion sco e in lac a ing Medi e anean bu alo by Visual Image 452 Analysis. Li es ock Science. 113, pp. 1–7. h ps://doi.o g/10.1016/j.li sci.2007.05.018 453
Pallo ino, F., S e i, R., Menesa i, P., An onucci, F., Cos a, C., Figo illi, S., Ca illo, G., 2015. 454 Compa ison be ween manual and s e eo ision body ai s measu emen s o Lipizzan 455
23
ho ses. Compu e s and Elec onics in Ag icul u e. 118, pp. 408–413. 456 h ps://doi.o g/10.1016/j.compag.2015.09.019 457
Pezzuolo, A., Gua ino, M., Sa o i, L., Ma inello, F., 2018. A Feasibili y s udy on he use o a 458 s uc u ed ligh dep h-came a o h ee-dimensional body measu emen s o dai y cows in 459 ee-s all ba ns. Senso s. 18 (2), pp. 673. h ps://doi.o g/10.3390/s18020673 460
P eisinge , R., Wilkens, J., Kalm, E., 1991. Es ima ion o gene ic pa ame e s and b eeding 461 alues o con o ma ion ai s o oals and ma es in he T akehne popula ion and hei 462 p ac ical implica ions. Li es ock P oduc ion Science. 29 (1), pp. 77-86. 463 h ps://doi.o g/10.1016/0301-6226(91)90121-6 464
Poly, J., Pou ous, M., Calomi i, S., Suzanne C., 1967. Le con ole lai ie mensual al e né (AT). 465 I._P écision is-a- is d’un con ole mensual ou bimes iel pou la p oduci on “de lai en 466 305 jou s”. Ann. Zoo ech. 16, pp. 183–190. 467
Sánchez, M.J., Gómez, M.D., Molina, A., Vale a, M., 2013. Gene ic analyses o linea 468 con o ma ion ai s in Pu a Raza Español ho ses. Li es ock Science. 157, pp. 57–64. 469 h ps://doi.o g/10.1016/j.li sci.2013.07.010 470
Sánchez-Gue e o, M.J., Molina, A., Gómez, M.D., Peña, F., Vale a, M., 2016. Rela ionship 471 be ween mo phology and pe o mance: Signa u e o mass-selec ion in Pu a Raza Español 472 ho se. Li es ock Science. 185, pp.148–155. h ps://doi.o g/10.1016/j.li sci.2016.01.003 473
Sánchez-Gue e o, M.J., Ce an es, I., Molina, A., Gu ié ez, J.P., Vale a, M., 2017. Designing 474 an ea ly selec ion mo phological linea ai s index o d essage in he Pu a Raza Español 475 ho se. Animal. 11(6), pp. 948-957. h ps://doi.o g/10.1017/S1751731116002214 476
Sánchez-Gue e o, M.J., Neg o-Rama, S., Daymas-Pey ás, S., Solé-Be ga, M., Azo -O iz, P.J., 477 Vale a-Có doba, M., 2018. Mo phological and gene ic di e si y o Pu a Raza Español 478 ho se ega ding o he coa colou . Animal Science Jou nal. 90, pp.14–22. 479 h ps://doi.o g/10.1111/asj.13102 480
Salau, J., Haas, J.H., Junge, W., Thalle , G., 2017. A mul i-kinec cow scanning sys em: 481 calcula ing linea ai s om manually ma ked eco dings o hols ein- iesian dai y cows. 482 Biosys em Enginee ing. 157, pp. 92–98. 483 h ps://doi.o g/10.1016/j.biosys emseng.2017.03.001 484
Shi, C., Teng, G., Li, Z., 2016. An app oach o pig weigh es ima ion using binocula s e eo 485 sys em based on LabVIEW. Compu e s and Elec onics in Ag icul u e. 129, pp. 37–43. 486 h ps://doi.o g/10.1016/j.compag.2016.08.012 487
Solé, M., San os, R., Gómez, M.D., Galis eo, A.M., Vale a, M., 2013. E alua ion o con o ma ion 488 agains ai s associa ed wi h d essage abili y in un idden Ibe ian ho ses a he o . 489 Resea ch in Ve e ina y Science. 95, pp. 660–666. 490 h ps://doi.o g/10.1016/j. sc.2013.06.017 491
Solé, M., Sánchez, M.J., Vale a, M., Molina, A., Azo , P.J., Sölkne , J., Mészá os, G., 2017. 492 Assessmen o spo i e longe i y in Pu a Raza Español d essage ho ses. Li es ock 493 Science. 203, pp. 69-75. h ps://doi.o g/10.1016/j.li sci.2017.07.007 494
Song, X., Bokke s, E.A.M., an de Tol, P.P.J., Koe kamp, P.W.G.G., an Mou ik, S., 2018. 495 Au oma ed body weigh p edic ion o dai y cows using 3-dimensional ision. Jou nal o 496 Dai y Science.101 (5), pp. 4448–4459. h ps://doi.o g/10.3168/jds.2017-13094 497
Viazzi, S., Ismayilo a, G., Oczak, M., Sonoda, L.T., Fels, M., Gua ino, M., V anken, E., Ha ung, 498 J., Bah , C., Be ckmans, D., 2014. Image ea u e ex ac ion o classi ica ion o agg essi e 499
24
in e ac ions among pigs. Compu e s and Elec onics in Ag icul u e. 104, pp. 57–62. 500 h ps://doi.o g/10.1016/j.compag.2014.03.010 501
Wu, J., Tille , R., McFa lane, N., Ju, X., Siebe , J.P., Scho ield, P., 2004. Ex ac ing he h ee-502 dimensional shape o li e pigs using s e eo pho og amme y. Compu e s and Elec onics 503 in Ag icul u e. 44, pp. 203–222. h ps://doi.o g/10.1016/j.compag.2004.05.003 504
505
506