senso s
A icle
Analysis o he Func ionali y o he Feed Chain in
Oli e Pi ing, Slicing and S u ing Machines by IoT,
Compu e Vision and Neu al Ne wo k Diagnosis
Albe o Lucas Pascual 1, An onio Madueño Luna 2, Manuel de Jóda Láza o 1,* ,
JoséMiguel Molina Ma ínez 1, An onio Ruiz Canales 3, JoséMiguel Madueño Luna 4
and Me i xell Jus icia Sego ia 3
1Food Enginee ing Depa men , Technical Uni e si y o Ca agena, 30203 Ca agena, Spain;
[email p o ec ed] (A.L.P.); [email p o ec ed] (J.M.M.M.)
2Ae ospace Enginee ing and Fluid Mechanical Depa men , Uni e si y o Se ille, 41013 Se ille, Spain;
[email p o ec ed]
3Enginee ing Depa men , Miguel He nández Uni e si y o Elche, 03312 O ihuela, Spain;
[email p o ec ed] (A.R.C.); [email p o ec ed] (M.J.S.)
4G aphics Enginee ing Depa men , Uni e si y o Se ille, 41013 Se ille, Spain; [email p o ec ed]
*Co espondence: [email p o ec ed]
Recei ed: 15 Janua y 2020; Accep ed: 6 Ma ch 2020; Published: 10 Ma ch 2020
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Abs ac :
Oli e pi ing, slicing and s u ing machines (DRR in Spanish) a e cha ac e ized by he
ac ha hei op imal unc ioning is based on app op ia e adjus men s. T adi ional sys ems a e no
comple ely eliable because hei minimum e o a e is 1–2%, which can esul in ui loss, since he
pi ing p ocess is no in allible, and ood sa e y issues can a ise. Such minimum e o s a e impossible
o emo e h ough mechanical adjus men s. In o de o achie e his objec i e, an inno a i e solu ion
mus be p o ided in o de o emo e e o s a ope a ing speed a es o e 2500 oli es/min. This wo k
analyzes he app op ia e placemen o oli es in he pocke s o he eed chain by using he ollowing
i ems: (1) An IoT Sys em o con ol he DRR machine and he da a analysis. (2) A compu e ision
sys em wi h an ex e nal sho came a and a LED ligh ing sys em, which akes a pic u e o e e y
pocke passing in on o he came a. (3) A chip wi h a neu al ne wo k o classi ica ion ha , once
ained, classi ies be ween ou possible pocke cases: emp y, no mal, inco ec ly de-s oned oli es a
any angles (also known as a “boa ”), and an anomalous case ( o eign elemen s such as lea s, small
b anches o s ones, wo oli es o small pa s o oli es in he same pocke ). The main objec i e o
his pape is o illus a e how wi h he use o a sys em based on IoT and a physical chip (Neu oMem
CM1K, Gene al Vision Inc.) wi h neu al ne wo ks o so ing pu poses, i is possible o op imize he
unc ionali y o his ype o machine by emo ely analyzing he da a ob ained. The use o classi ying
ha dwa e allows i o wo k a he nominal ope a ing speed o hese machines. This would be limi ed
i o he classi ying echniques based on so wa e we e used.
Keywo ds:
In e ne o hings (IoT); able oli e pi ing; slicing and s u ing machines; a i icial neu al
ne wo ks (ANNs); CM1K chip; In el Cu ie chip; Teensy
1. In oduc ion
Oli es we e pi ed and s u ed by hand un il he 1970s. As labo cos s g adually inc eased,
he need o mechanize bo h p ocesses a ose. The indus ializa ion o he oli e indus y p og essed a
a slow pace, and he i s con inuous oli e pi ing machines appea ed in 1975 [
1
]. Cu en models ha e
imp o ed and a e able o pi oli es a a a e up o 2500 oli es/min. Howe e , he e has been a lack o
elec onic imp o emen s because he ongoing mechanical imp o emen s in oli e pi ing machines
Senso s 2020,20, 1541; doi:10.3390/s20051541 www.mdpi.com/jou nal/senso s
Senso s 2020,20, 1541 2 o 22
ha e limi ed he inco po a ion o new echnologies and ine adjus men s. I is wo h men ioning
howe e some o he ew ad ances in his a ea, including pe o mance op imiza ion and emo e e o
de ec ion [
2
]. The oli e pi ing minimum e o a e o adi ional sys ems is 1–2% (i he machine
is co ec ly adjus ed) and canno be imp o ed h ough mechanical adjus men s al hough his e o
can inc ease due o he poo calib a ion o an oli e pi ing machine. The consequences o hese e o
inc eases include a g ea e ui loss and ood sa e y issues, because he pi ing p ocess is no comple ely
ensu ed, i may esul in a low-quali y p oduc . The g owing compe i i eness in p oduce coun ies,
due o a globalized ma ke , and he inc ease o ood sa e y measu es ha e led o new echnological
solu ions. These solu ions a e aimed a imp o ing he eliabili y o oli e pi ing machines du ing
he pi ing p ocess by emo ing he s one comple ely and inc easing hei p oduc i i y o be e
compe i ion wi h o he p oduce s.
As a biological p oduc cha ac e ized by a he e ogeneous mo phology, oli es challenge de ec ion
echnologies. The e o e, neu al ne wo ks a e he ools chosen o iden i y e e y possible case. Cases
using deep lea ning and compu e ision ha e been con i med o deli e g ea esul s and a e
p omising solu ions o sol ing he a o emen ioned p oblem. Yunchao e al. [
3
] p esen ed an a icle o
in e es on he use o eal- ime de ec ion in ecycled agg ega e conc e e echnology. Howe e , o his
pa icula case, i does no p io i ize he p ocessing speed. The e o e, his echnology does no ha e
he same applicabili y in he p esen a icle. Ano he s udy ha could be conside ed o de e mining
an applicable solu ion is he analysis de eloped by Mingyou e al. [
4
], who used a mul i-came a
sys em, which is in e es ing o he analysis o se e al pi ing machines o inc ease he pe cep ion
ange o ision. Howe e , he use o se e al came as is no he subjec o his s udy. The e o e, i is
necessa y o explo e o he solu ions ha p o ide su icien p ocessing speed o analyze images a he
speed p esc ibed by he pi ing machines. Al hough hese s udies use in e es ing echnology o he
de ec ion and classi ica ion o images, hei esul s a e no applicable he e because p ocessing speed is
no ele an .
The e a e applica ions which use compu e isions wi h apid de ec ion echnologies o indus ial
pu poses like p oduc classi ica ion o image analysis. These sys ems use mo phological ope a ions
o ex ac he sough cha ac e is ics in he image h ough il e s and speci ic analysis algo i hms like
Canny Edge, O su, local bina y pa e n algo i hm, K-means, e c. In o de o de elop hese algo i hms,
speci ic so wa e is used, such as he in eg a ed de elopmen se ing o Mic oso Visual S udio and
speci ic compu e ision lib a ies, as in he case wi h Openc . Maoyon, e al. [
5
] p oposes a s udy using
hese ypes o echniques in he classi ica ion o apples on an indus ial le el. Al hough he desc ibed
p ocedu es a e o g ea in e es , hese ypes o echniques equi e high-pe o mance compu ing
ha dwa e and ele a ed cos s o he ype o applica ion e alua ed in he cu en analysis, whe e mo e
economic solu ions and educed ha dwa e size a e sough . Lucas e al. [
6
] p opose a speci ic example
done wi h oli es which uses hese as de ec ion echniques based on C++ p og amming language and
OpenCV a i icial ision lib a ies.
Speci ic s udies in he ag i- ood sec o and he ela ed beha io o neu al ne wo ks ha e been
conside ed. I is wo h men ioning he s udies conduc ed by Guichao e al. [
7
–
9
] on de ec ion sys ems
o he a i icial isualiza ion o ui s h ough RGB space colo analysis and obo ic sys ems. In he
case o he classi ica ion o apples, Yang e al. [
10
] p o ide posi i e esul s. Yang’s in es iga ion used
h ee laye s o 9-6-3 neu ons, wi h 96.6% accu acy. Naga a e al. [
11
] de eloped a g ading sys em o
ui and ege ables using neu al ne wo k echnologies, ob aining a high pe cen age o accu acy o
s awbe ies and g een peppe s (94% o 98% and 89%, espec i ely). Likewise, Beh ooz e al. [
12
]
applied machine ision and a i icial neu al ne wo k (ANN) o modelling and con olling he g ape
d ying p ocess, o e ing a new me hod o p edic i e modelling o he g ape d ying p ocess o he
on-line moni o ing and con olling o he p ocess.
The e ha e been some echniques used o classi y able oli es using compu e ision and neu al
ne wo ks. Ga ika e al. [
13
] p oposed an in e es ing pape abou oli e ui ecogni ion using neu al
ne wo ks. Oli e ui ecogni ion is pe o med by analyzing RGB images aken om oli e ees. Ano he
Senso s 2020,20, 1541 3 o 22
s udy used a neu al ne wo k based on he backp opaga ion me hod. Mancuso e al. [
14
] iden i ied
oli e (Olea eu opaea) cul i a s using a i icial neu al ne wo ks. Backp opaga ion neu al ne wo ks
(BPNNs) we e used o dis inguish 10 oli e (Olea eu opaea L.) cul i a s ha o igina e h oughou he
Medi e anean basin. The book Compu e Vision Technology o Food Quali y E alua ion, 2nd Edi ion [
15
],
includes a speci ic chap e based on he di e en echnologies use o analyze he quali y o ood.
Chap e s 11 o 13 desc ibe he quali y e alua ions o apples, ci us ui s, and s awbe ies, espec i ely.
O pa icula in e es o he p oblem a hand, Chap e 14 [
16
] explains he classi ica ion and e alua ion
o able oli es and desc ibes how o classi y hem by colo , shape, o ex e nal de ec s made by insec s.
The usual way o classi y oli es is by using compu e ision. Tha pape analyzes he images cap u ed
by a came a connec ed o a PC; hese images allow he analysis o 66 oli es pe ma ix. In ha
pape , he au ho used a Bayesian ma h model o p e-classi ica ion o pe o m his p ocess. Neu al
ne wo k so wa e was used, wi h 15 so ing pa ame e s and a hidden laye . The esul was success ul.
The ne wo k was able o classi y mo e han ou ypes o oli es. The esul s, howe e , could be
imp o ed by using high- esolu ion images.
P e ious examples employed so wa e-based neu al ne wo ks, whe e he p ocessing speed is no
ele an . Thus, al hough he de ec ion and classi ica ion echnology in hese s udies is applicable, i is
necessa y o look o echnology ha achie es high p ocessing speeds. In addi ion, i is p e e able o
use sys ems wi h limi ed physical implemen a ion ha can be ins alled wi h low in usion a lowe
cos s, ins ead o adi ional ha dwa e, such as indus ial PC’s. The e o e, neu al ne wo k echnology
based on a physical chipse ensu es i s success ul implemen a ion and low cos .
The possible applica ions o his ype o chip a e e y b oad. The ollowing a icle is in e es ing
because CM1K neu al chip o Gene al Vision Inc. (Pe aluma, CA 94952 USA) was implemen ed in
a common indus ial p ocess used o ill bo les in ac o ies. This p ocess is qui e simple; howe e ,
i may be necessa y o use an in elligen de ice o inspec he p ocess and ensu e ood secu i y [
17
].
In ano he example, pa allel neu al ne wo k chips we e used o sho e o ish inspec ion be o e
ille ing [
18
]. Each ne wo k chip sys em uses ou neu al ne wo k chips (accoun ing o 312 neu ons)
based on a na i ely pa allel, ha d-wi ed a chi ec u e ha pe o ms eal- ime lea ning and nonlinea
classi ica ion (RBF). The use o CM1K in eal ime e i ies i s use o he pu poses o he p esen s udy.
The combina ion o compu e ision and neu al ne wo ks o e s a way o pe o m asks ha could
be mo e complex. Liu e al. [
19
] p oposed a neu al ne wo k chip o license pla e ecogni ion. This chip
combines a ideo image-p ocessing module wi h a neu al ne wo k module by using equalized image
p ocessing algo i hms and ne wo k classi ica ion algo i hms. San u Sa da e al. [
20
] published a pape
based on au oma ed acial ecogni ion, which is a echnique employed in a wide ange o p ac ical
applica ions, including pe sonnel access con ol and iden i ica ion sys ems. Image ecogni ion is
simple han image p ocessing me hods o acial ecogni ion, mainly due o he lack o a ixed pa e n
o compa ison pu poses; hese applica ions e eal he emendous possibili ies o CM1K.
La e CM1K chipse s ha e deli e ed sa is ac o y esul s. An in e es ing aspec o conside in
u u e esea ch would be o compa ison ou chosen chip wi h hese o he echnologies desc ibed in he
ollowing s udies [21–30].
The use o he IoT is widely ex ended nowadays and he e a e nume ous cases, o example [
31
–
35
]
in p ecision ag icul u e, i iga ion, empe a u e con ol, moni o ing o he ag icul u al p oduc ion
p ocess o au oma ed-oli e-chain. In he case p esen ed in his pape , we a e going o be able o
moni o and analyze he ope a ion o DRR machines h ough he in e ne .
2. Ma e ials and Me hods
In his sec ion, he in e nal s uc u e o he CM1K chip (Sec ions 2.1 and 2.2) and he implemen a ion
o Ma lab o neu al ne wo ks wi h cha ac e is ics simila o hose o he CM1K chip a e analyzed
(Sec ion 2.3).
A chip such as he CM1K has a limi a ion in ha he en y ec o i can suppo is educed in his
case (256 by es), meaning ha i i is going o p ocess an image, ha image canno be la ge in pixel
Senso s 2020,20, 1541 4 o 22
size han his alue (256 by es) and ha i could only be B/W wi h 256 le els o g ay. In o de o assu e
ha a neu al ne wo k wi h hese cha ac e is ics (equipped wi h high speed as i is pu e ha dwa e, bu
o limi ed ec o en y) can be accep able o he pu pose o classi ica ion in oli e pi ing machines,
a neu al ne wo k is simula ed in Ma lab which wo ks wi h a delay and allows us o assu e ha co ec
classi ica ion will be ca ied ou in a ious ca ego ies wi h images o jus 16 ×16 pixels in B/W.
In Sec ions 2.4 and 2.5 he physical implemen a ion o he classi ica ion sys em is shown wi h
a eal oli e pi ing machine and in Sec ion 2.6 he comme cial e sion o he CM1K chip ha has been
used in he ials is analyzed. Fo hese sec ions, he desc ip ion supplied in [
36
] will be used by he
au ho s o his pape .
In Sec ion 2.7 he communica ion ha dwa e o he CM1K-PC o classi ica ion in eal ime is
desc ibed and las ly, in Sec ion 2.8, he IoT sys em o con ol he oli e pi ing, slicing and s u ing
machine (DRR in Spanish) and da a analysis is analyzed.
2.1. Neu al Ne wo k
In his wo k a supe ised neu al ne wo k wi h backp opaga ion has been chosen because he e
a e no ele an di e ences wi h espec o he calcula ion p ocesses used by o he ne wo ks. Mo eo e ,
his sys em is one o he mos widesp ead sys ems used o image iden i ica ion pu poses, and i is
widely used in he MATLAB Neu al Ne wo k Toolbox employed in his pape . Backp opaga ion is he
mos e icien way o se his alue [
37
]. Fi s , e o s a e calcula ed in ou pu uni s by conside ing he
di e ence be ween he desi ed and p ede e mined alues. Nex , hey p opaga e h ough he ne wo k
using he weigh s and ob ain he minimum alue in he mos op imal way.
The so wa e emula ion o a neu al ne wo k is compu a ionally demanding, which indica es
ha i s ope a ions will ake a long pe iod o ime o be ca ied ou . The use o a chip ha physically
implemen s a neu al ne wo k (as in his pape wi h he CM1K chip o In el Cu ie chip) will speed up
he aining and esponse p ocesses o he neu al ne wo k. A compa ison wi h o he exis ing ha dwa e
neu al ne wo ks like Google Co al Edge TPU [
38
], In el
®
Mo idius
™
Neu al Compu e S ick 2 [
39
] o
N idia-Je son-Nano [40] and op imized esul s will be p o ided in u u e pape s.
2.2. Ope a ion o he CM1K Chip
The CM1K is a pa e n ecogni ion accele a o chip ha is ainable in eal- ime by lea ning
examples [
41
]. I is a ully pa allel silicon neu al ne wo k o ei he lea ning o ecogni ion [
42
],
ha can s o e and p ocess in o ma ion simul aneously. I is composed o ou modules [
43
], has
wo possible classi ie s: K-nea es neighbo (KNN) o adial basis unc ion (RBF) [
44
] and uses he
“Winne -Takes-All” s a egy [
45
]. I has 1024 neu ons o 256 by es. In el Cu ie chip is a diminished
e sion wi h only 128 neu ons o 128 by es.
2.3. The MATLAB Neu al Ne wo k Toolbox
In his wo k we a e using he MATLAB Neu al Ne wo k [
46
] by using a speci ic ne wo k called
‘au oencode ’ o classi ica ion pu poses [
47
,
48
], i will allow us o analyze he e ec o image esolu ion
on he so ing capaci y o he ne wo k.
2.3.1. P elimina y Tes s: Maximum Resolu ion A ailable
The maximum esolu ion implemen ed unde came a boa d is 144
×
176 mee ing he size
equi emen s o he pocke acco ding o used lenses. A e he i s es using he abo e-men ioned
esolu ion, a 25,344 pixels a 1-by e colo dep h g ayscale image is ob ained, which implies a high
p ocessing a e (nea ly wo minu es using a 2.5 GHz CPU, RAM 8 GB, Mic oso Windows 10, Redmond,
Washing on, USA, and Ma hWo ks MATLAB 2019, Massachuse s, USA).
Senso s 2020,20, 1541 5 o 22
P elimina y es s sugges ha p ocessing op imiza ion measu es need o be ca ied ou . These
measu es will ocus on wo main aspec s:
•Es ablishing a egion o in e es (ROI) on he image.
•
Tes ing di e en esolu ions ha allow he iden i ica ion o he image a he minimum
p ocessing a e.
In addi ion o hese aspec s, i is impo an o conside he p ocessing a e o he physical chips:
In el Cu ie (128 neu ons wi h a 128-by e inpu ec o ) and Neu oMem CM1K (1024 neu ons wi h
a 256-by e inpu ec o ).
Conside ing he abo e-men ioned es ic ions, he maximum esolu ion o he image mus be
be ween 128 and 256 pixels o a squa e image, as inally used: 16x16 pixels o Neu oMem CM1K
256-by e chip and 11 ×11 pixels (121 by es <128 by es) o In el Cu ie chip.
2.3.2. P elimina y Tes s: Minimum Resolu ion
Se e al es s o iden i y he minimum esolu ion accep ed by physical chips a e ca ied ou wi h
he pu pose o se ing he lowes p ocessing a e. To do so, eal images a e o be p ocessed in MATLAB
and i should be es ima ed i he sys em is able o iden i y hem.
The i s s ep is o se he ROI, which mus be a pa o he image wi h enough in o ma ion in o de
o iden i y he posi ion o he oli e in he pocke . The e o e, he oli e mus be comple ely displayed on
he image.
The ollowing Figu es 1and 2show some examples o 176
×
144 pixel-images wi h he (X,Y)
e e ence sys em. As shown in he igu es, he oli e is es ic ed in he X-axis ( he same pocke con ines
he oli e o i s space) and, he e o e, he selec ion o he pa ame e s o ROI in ha axis is cons an . The e
is a possibili y o he oli e o mo e in Y-axis, consequen ly leading o a de ia ion o he dimensions
o ROI. Howe e , i is empi ically p o en ha , due o he inclina ion o he chain and he upwa d
mo emen o he oli e, g a i y, ine ia and some elemen s o he machine (b ushes, o example) o ces
he oli e o s ay in a simila posi ion. Va ia ions may be iden i ied in o ien a ion, which is he subjec
o his pape .
Senso s 2020, 20, x FOR PEER REVIEW 5 o 22
 Tes ing di e en esolu ions ha allow he iden i ica ion o he image a he minimum
p ocessing a e.
In addi ion o hese aspec s, i is impo an o conside he p ocessing a e o he physical chips:
In el Cu ie (128 neu ons wi h a 128-by e inpu ec o ) and Neu oMem CM1K (1024 neu ons wi h a
256-by e inpu ec o ).
Conside ing he abo e-men ioned es ic ions, he maximum esolu ion o he image mus be
be ween 128 and 256 pixels o a squa e image, as inally used: 16x16 pixels o Neu oMem CM1K
256-by e chip and 11 × 11 pixels (121 by es < 128 by es) o In el Cu ie chip.
2.3.2. P elimina y Tes s: Minimum Resolu ion
Se e al es s o iden i y he minimum esolu ion accep ed by physical chips a e ca ied ou wi h
he pu pose o se ing he lowes p ocessing a e. To do so, eal images a e o be p ocessed in
MATLAB and i should be es ima ed i he sys em is able o iden i y hem.
The i s s ep is o se he ROI, which mus be a pa o he image wi h enough in o ma ion in
o de o iden i y he posi ion o he oli e in he pocke . The e o e, he oli e mus be comple ely
displayed on he image.
The ollowing Figu es 1 and 2 show some examples o 176 × 144 pixel-images wi h he (X,Y)
e e ence sys em. As shown in he igu es, he oli e is es ic ed in he X-axis ( he same pocke
con ines he oli e o i s space) and, he e o e, he selec ion o he pa ame e s o ROI in ha axis is
cons an . The e is a possibili y o he oli e o mo e in Y-axis, consequen ly leading o a de ia ion o
he dimensions o ROI. Howe e , i is empi ically p o en ha , due o he inclina ion o he chain and
he upwa d mo emen o he oli e, g a i y, ine ia and some elemen s o he machine (b ushes, o
example) o ces he oli e o s ay in a simila posi ion. Va ia ions may be iden i ied in o ien a ion,
which is he subjec o his pape .
Y
X
Y
X
Figu e 1. Example o he use o egion o in e es (ROI) in an oli e in a no mal posi ion. The ed poin
co esponds o he o igin o coo dina es o pixels. The g een ec angle comp ises he pocke wi hin
i s walls and dep h and limi ed by he p e ious and he nex pocke o he eed chain. The ed
ec angle is he ROI.
Figu e 1.
Example o he use o egion o in e es (ROI) in an oli e in a no mal posi ion. The ed poin
co esponds o he o igin o coo dina es o pixels. The g een ec angle comp ises he pocke wi hin i s
walls and dep h and limi ed by he p e ious and he nex pocke o he eed chain. The ed ec angle is
he ROI.

Senso s 2020,20, 1541 6 o 22
Senso s 2020, 20, x FOR PEER REVIEW 6 o 22
Figu e 2. Example o he use o ROI in an oli e in a “boa ” posi ion be ween [80°, 90°] and [−80°, −90°].
A e p e ious conside a ions, he ollowing condi ions a e se ou (measu ed in pixels and
e e ed o he o igin o coo dina es, Figu e 3):
Figu e 3. Selec ed ROI.
Once he ROI pa ame e s in X and Y a e se , se e al es s a e ca ied ou in o de o es ablish i
he sys em is s ill able o iden i y images using he selec ed esolu ions. A se o 10,000 images o 176
× 144 pixels in B/W o he oli es in no mal posi ion and boa and emp y bucke s a e a ailable, he e
a e no anomalous cases (i.e., lea es, small b anches, s ones, double oli es, small pa s) in his se . This
se is a ailable o o he esea che s [49]. Wi h an applica ion in Ma lab, each image is bina ized and
he o ien a ion o each oli e is calcula ed. Fo he es s desc ibed in his wo k, hey a e andomly
selec ed in g oups o 300 oli es. The o ien a ion o each oli e (no mal, in e media e posi ion o boa )
is de ined by he posi ion ela i e o he punches (Figu e 4).
Figu e 2.
Example o he use o ROI in an oli e in a “boa ” posi ion be ween [80
◦
, 90
◦
] and [
−
80
◦
,
−
90
◦
].
A e p e ious conside a ions, he ollowing condi ions a e se ou (measu ed in pixels and
e e ed o he o igin o coo dina es, Figu e 3):
Senso s 2020, 20, x FOR PEER REVIEW 6 o 22
Figu e 2. Example o he use o ROI in an oli e in a “boa ” posi ion be ween [80°, 90°] and [−80°, −90°].
A e p e ious conside a ions, he ollowing condi ions a e se ou (measu ed in pixels and
e e ed o he o igin o coo dina es, Figu e 3):
Figu e 3. Selec ed ROI.
Once he ROI pa ame e s in X and Y a e se , se e al es s a e ca ied ou in o de o es ablish i
he sys em is s ill able o iden i y images using he selec ed esolu ions. A se o 10,000 images o 176
× 144 pixels in B/W o he oli es in no mal posi ion and boa and emp y bucke s a e a ailable, he e
a e no anomalous cases (i.e., lea es, small b anches, s ones, double oli es, small pa s) in his se . This
se is a ailable o o he esea che s [49]. Wi h an applica ion in Ma lab, each image is bina ized and
he o ien a ion o each oli e is calcula ed. Fo he es s desc ibed in his wo k, hey a e andomly
selec ed in g oups o 300 oli es. The o ien a ion o each oli e (no mal, in e media e posi ion o boa )
is de ined by he posi ion ela i e o he punches (Figu e 4).
Figu e 3. Selec ed ROI.
Once he ROI pa ame e s in X and Y a e se , se e al es s a e ca ied ou in o de o es ablish i he
sys em is s ill able o iden i y images using he selec ed esolu ions. A se o 10,000 images o 176
×
144
pixels in B/W o he oli es in no mal posi ion and boa and emp y bucke s a e a ailable, he e a e no
anomalous cases (i.e., lea es, small b anches, s ones, double oli es, small pa s) in his se . This se is
a ailable o o he esea che s [
49
]. Wi h an applica ion in Ma lab, each image is bina ized and he
o ien a ion o each oli e is calcula ed. Fo he es s desc ibed in his wo k, hey a e andomly selec ed
in g oups o 300 oli es. The o ien a ion o each oli e (no mal, in e media e posi ion o boa ) is de ined
by he posi ion ela i e o he punches (Figu e 4).
Senso s 2020,20, 1541 7 o 22
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
•[0◦, 10◦] and [0◦,−10◦] (No mal)
•[10◦, 20◦] and [−10◦,−20◦] (In e media e)
•[20◦,−30◦] and [−20◦,−30◦] (In e media e)
•[30◦,−40◦] and [−30◦,−40◦] (In e media e)
•[40◦,−50◦] and [−40◦,−50◦] (In e media e)
•[50◦,−60◦] and [−50◦,−60◦] (In e media e)
•[60◦,−70◦] and [−60◦,−70◦] (In e media e)
•[70◦,−80◦] and [−70◦,−80◦] (In e media e)
•[80◦,−90◦] and [−80◦,−90◦] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0◦, 10º] ANGLE [0◦,−10◦] ANGLE [10◦,20◦] ANGLE [−10◦,−20◦]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
ANGLE [20◦,30◦] ANGLE [−20◦,−30◦] ANGLE [30◦,40◦] ANGLE [-30◦,-40◦]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020, 20, x FOR PEER REVIEW 7 o 22
Punch
needle
Oli e in no mal
posi ion
Oli e in boa
posi ion
Ha o deboning
Figu e 4. Punch needles in an oli e pi ing machine.
The classi ica ion by angles is as ollows (see Table 1):
 [0°, 10°] and [0°, −10°] (No mal)
 [10°, 20°] and [−10°, −20°] (In e media e)
 [20°, −30°] and [−20°, −30°] (In e media e)
 [30°, −40°] and [−30°, −40°] (In e media e)
 [40°, −50°] and [−40°, −50°] (In e media e)
 [50°, −60°] and [−50°, −60°] (In e media e)
 [60°, −70°] and [−60°, −70°] (In e media e)
 [70°, −80°] and [−70°, −80°] (In e media e)
 [80°, −90°] and [−80°, −90°] (Boa )
Table 1. Images (bo h o iginal and bina ized) classi ied acco ding o angula in e als.
ANGLE [0°, 10º]
ANGLE [0°,−10°]
ANGLE [10°,20°]
ANGLE [−10°,−20°]
ANGLE [20°,30°]
ANGLE [−20°,−30°]
ANGLE [30°,40°]
ANGLE [-30°,-40°]
Senso s 2020,20, 1541 8 o 22
Table 1. Con .
ANGLE [40◦,50◦] ANGLE [−40◦,−50◦] ANGLE [50◦,60◦] ANGLE [−50◦,−60◦]
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
ANGLE [60◦,70◦] ANGLE [−60◦,−70◦] ANGLE [70◦,80◦] ANGLE [−70◦,−80◦]
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
ANGLE [80◦,90◦] o
[−80◦,−90◦]
Senso s 2020, 20, x FOR PEER REVIEW 8 o 22
ANGLE [40°,50°]
ANGLE [−40°,−50°]
ANGLE [50°,60°]
ANGLE [−50°,−60°]
ANGLE [60°,70°]
ANGLE [−60°,−70°]
ANGLE [70°,80°]
ANGLE [−70°,−80°]
ANGLE [80°,90°] o
[−80°,−90°]
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
 Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
 Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
 The ha dwa e ha le s he came a [50] con ol and ligh ing based on a magne ic senso ha
allows o ob ain images o each bucke acing he came a (Figu es 5 and 6).
2.4. Ha dwa e Used in Image Cap u e
The sys em de eloped has wo unc ions:
•
Ob ain images o de e ed analysis wi h Ma lab, he In el Cu ie and CM1K neu al chips o
e alua e he ope a ion o he la e .
•
Cha ac e ize he eal- ime ope a ion h ough IoT o he pi ing machines o an oli e ac o y ha
each speeds o up o 2500 oli es/min.
The sys em consis s o wo pa s:
•
The ha dwa e ha le s he came a [
50
] con ol and ligh ing based on a magne ic senso ha allows
o ob ain images o each bucke acing he came a (Figu es 5and 6).
•
A pe sonal compu e (PC) which allows, o he i s case, he s o age o images o de e ed
analysis and in he second case oge he wi h a CM1K chip o eal- ime analysis o he same o e
he in e ne wi h an applica ion de eloped in Q C ea o [51] (Figu e 7).
Senso s 2020,20, 1541 9 o 22
Senso s 2020, 20, x FOR PEER REVIEW 9 o 22
Suppo
pla e
Magne ic senso
PVC adap e
Figu e 5. Magne ic senso used o de ec he passage o he pocke s in he chain.
Powe supply 24V o
ci cui clean ision
a ea
Ci cui o con ol LED
and came a
Ci cui o con ol
magne ic senso
Powe supply 12V o
con ol leds
Powe supply 5V o
oc ocouple ci cui o
came a igge
Figu e 6. Elec onic ci cui o he ex e nal igge and LED.
 A pe sonal compu e (PC) which allows, o he i s case, he s o age o images o de e ed
analysis and in he second case oge he wi h a CM1K chip o eal- ime analysis o he same
o e he in e ne wi h an applica ion de eloped in Q C ea o [51] (Figu e 7).
Figu e 5. Magne ic senso used o de ec he passage o he pocke s in he chain.
Senso s 2020, 20, x FOR PEER REVIEW 9 o 22
Suppo
pla e
Magne ic senso
PVC adap e
Figu e 5. Magne ic senso used o de ec he passage o he pocke s in he chain.
Powe supply 24V o
ci cui clean ision
a ea
Ci cui o con ol LED
and came a
Ci cui o con ol
magne ic senso
Powe supply 12V o
con ol leds
Powe supply 5V o
oc ocouple ci cui o
came a igge
Figu e 6. Elec onic ci cui o he ex e nal igge and LED.
 A pe sonal compu e (PC) which allows, o he i s case, he s o age o images o de e ed
analysis and in he second case oge he wi h a CM1K chip o eal- ime analysis o he same
o e he in e ne wi h an applica ion de eloped in Q C ea o [51] (Figu e 7).
Figu e 6. Elec onic ci cui o he ex e nal igge and LED.
Senso s 2020,20, 1541 16 o 22
Table 4. Resul s o he aining pa e ns using a esolu ion o 1 6 ×16 pixels.
Type o Tes Deg ees In e media e Posi ion Simple
Neu on 66 Neu ons 24 Neu ons 4
Boa d Vec o Repea abili y E o Repea abili y E o Repea abili y E o
B ainca d
(CM1K) 256
18%13%11%
29%24%20%
3 10% 3 4% 3 0%
4 12% 4 3% 4 2%
5 13% 5 4% 5 1%
6 13% 6 5% 6 1%
7 15% 7 2% 7 0%
8 16% 8 5% 8 1%
9 16% 9 8% 9 1%
10 13% 10 2% 10 0%
To als 12.43% 4% 0.63%
The op imal op ion is B ainca d 256 chip (0.63% e o a e <1%) and only needs ou neu ons o
classi y be ween no mal, emp y and boa oli es. I we use an In el cu ie chip e o a e is 4.80% <5%
and only needs ou neu ons oo. This means ha he sugges ed sys em is capable o de ec ing up o
a 95–99% o he de iciencies (boa s and emp y) p esen ed by he machine.
3.3. Analysis o he Resul s o he Ope a ion o he DRR Machine
An applica ion (“Logge DRR”) has been de eloped o analyze he ope a ion o he oli e pi ing
machine wi h GUI by Ma lab (Figu e 15). The mos ele an pa ame e s o his applica ion will be
indica ed below:
Senso s 2020, 20, x FOR PEER REVIEW 16 o 22
Table 4. Resul s o he aining pa e ns using a esolu ion o 1 6 × 16 pixels.
Type o Tes
Deg ees
In e media e Posi ion
Simple
Neu on
66
Neu ons
24
Neu ons
4
Boa d
Vec o
Repea abili y
E o
Repea abili y
E o
Repea abili y
E o
B ainca d
(CM1K)
256
1
8%
1
3%
1
1%
2
9%
2
4%
2
0%
3
10%
3
4%
3
0%
4
12%
4
3%
4
2%
5
13%
5
4%
5
1%
6
13%
6
5%
6
1%
7
15%
7
2%
7
0%
8
16%
8
5%
8
1%
9
16%
9
8%
9
1%
10
13%
10
2%
10
0%
To als
12.43%
4%
0.63%
The op imal op ion is B ainca d 256 chip (0.63% e o a e < 1%) and only needs ou neu ons o
classi y be ween no mal, emp y and boa oli es. I we use an In el cu ie chip e o a e is 4.80% < 5%
and only needs ou neu ons oo. This means ha he sugges ed sys em is capable o de ec ing up o
a 95–99% o he de iciencies (boa s and emp y) p esen ed by he machine.
3.3. Analysis o he Resul s o he Ope a ion o he DRR Machine
An applica ion (“Logge DRR”) has been de eloped o analyze he ope a ion o he oli e pi ing
machine wi h GUI by Ma lab (Figu e 15). The mos ele an pa ame e s o his applica ion will be
indica ed below:
Ins an aneous o
accumula ed da a
scale
Selec ed pe iod,
da e and ime
ange.
Ins an aneous o
accumula ed g aph:
Boa s and Doubles
Figu e 15. G aphical Use In e ace (GUI) c ea ed in Ma lab o analyze da a om he “DRR machines”.
The ep esen a i e da a is indica ed in he ollowing box (Figu e 16:
 Speed: Indica ed in pocke s pe minu e
 P oduc ion: Real alue in oli es pe minu e (wi hou emp y pocke )
 “Boa ” oli es, no mal, double oli es, emp y, small pieces, anomalies: Pe cen age o he o al
p oduc ion.
 Accumula ed alues: Da a added in he selec ed pe iod.
Figu e 15.
G aphical Use In e ace (GUI) c ea ed in Ma lab o analyze da a om he “DRR machines”.
The ep esen a i e da a is indica ed in he ollowing box (Figu e 16:
•Speed: Indica ed in pocke s pe minu e
•P oduc ion: Real alue in oli es pe minu e (wi hou emp y pocke )
•
“Boa ” oli es, no mal, double oli es, emp y, small pieces, anomalies: Pe cen age o he
o al p oduc ion.
•Accumula ed alues: Da a added in he selec ed pe iod.

Senso s 2020,20, 1541 17 o 22
Senso s 2020, 20, x FOR PEER REVIEW 17 o 22
Figu e 16. Se ings dialogue box o he GUI om Ma lab.
In Figu e 17, a p ac ical example o use is shown. The e a e wo accumula ed g aphs, he oli e
g een one co esponding o “boa ” oli es de ec ed and he och e colo co esponding o double oli es
en e ing in he pocke . Each da a upda e poin co esponds o a coun o 1000 oli es.
Machine s op (1)
Recalib a ion s op (3)
Figu e 17. GUI o “boa ” oli es and doubles o he “DRR machine” diagnosis.
The spaces co espond o pe iods when he machine was s opped (1), his can be due o changes
in he eed chain (change o con aine whe e he oli es come om) o s ops o adjus he machine o
pe sonnel b eaks.
In (2), he machine is obse ed o ha e a misalignmen (due o he misalignmen o he b ush
which s aigh ens he oli es in he eeding chain), ollowed by a s op o ecalib a ion (3).
Immedia ely a e ha , we can obse e how he slope dec eases (4)→(5) which means he numbe o
“boa ” oli es has dec eased. Addi ionally, he numbe o double oli es ( a ious in he same pocke )
inc eases (6), meaning ha he ecalib a ion has no been op imal and he eed pla e has been poo ly
adjus ed.
Figu e 18 shows an example o a ious combined pa ame e s (boa accumula es, emp y
accumula ed, doubles accumula ed and small pa s accumula ed).
Figu e 16. Se ings dialogue box o he GUI om Ma lab.
In Figu e 17, a p ac ical example o use is shown. The e a e wo accumula ed g aphs, he oli e
g een one co esponding o “boa ” oli es de ec ed and he och e colo co esponding o double oli es
en e ing in he pocke . Each da a upda e poin co esponds o a coun o 1000 oli es.
Senso s 2020, 20, x FOR PEER REVIEW 17 o 22
Figu e 16. Se ings dialogue box o he GUI om Ma lab.
In Figu e 17, a p ac ical example o use is shown. The e a e wo accumula ed g aphs, he oli e
g een one co esponding o “boa ” oli es de ec ed and he och e colo co esponding o double oli es
en e ing in he pocke . Each da a upda e poin co esponds o a coun o 1000 oli es.
Machine s op (1)
Recalib a ion s op (3)
Figu e 17. GUI o “boa ” oli es and doubles o he “DRR machine” diagnosis.
The spaces co espond o pe iods when he machine was s opped (1), his can be due o changes
in he eed chain (change o con aine whe e he oli es come om) o s ops o adjus he machine o
pe sonnel b eaks.
In (2), he machine is obse ed o ha e a misalignmen (due o he misalignmen o he b ush
which s aigh ens he oli es in he eeding chain), ollowed by a s op o ecalib a ion (3).
Immedia ely a e ha , we can obse e how he slope dec eases (4)→(5) which means he numbe o
“boa ” oli es has dec eased. Addi ionally, he numbe o double oli es ( a ious in he same pocke )
inc eases (6), meaning ha he ecalib a ion has no been op imal and he eed pla e has been poo ly
adjus ed.
Figu e 18 shows an example o a ious combined pa ame e s (boa accumula es, emp y
accumula ed, doubles accumula ed and small pa s accumula ed).
Figu e 17. GUI o “boa ” oli es and doubles o he “DRR machine” diagnosis.
The spaces co espond o pe iods when he machine was s opped (1), his can be due o changes
in he eed chain (change o con aine whe e he oli es come om) o s ops o adjus he machine o
pe sonnel b eaks.
In (2), he machine is obse ed o ha e a misalignmen (due o he misalignmen o he b ush
which s aigh ens he oli es in he eeding chain), ollowed by a s op o ecalib a ion (3). Immedia ely
a e ha , we can obse e how he slope dec eases (4)
→
(5) which means he numbe o “boa ” oli es
has dec eased. Addi ionally, he numbe o double oli es ( a ious in he same pocke ) inc eases (6),
meaning ha he ecalib a ion has no been op imal and he eed pla e has been poo ly adjus ed.
Figu e 18 shows an example o a ious combined pa ame e s (boa accumula es, emp y
accumula ed, doubles accumula ed and small pa s accumula ed).
Senso s 2020,20, 1541 18 o 22
Senso s 2020, 20, x FOR PEER REVIEW 18 o 22
Fi s misalignmen
o he eed pla e (1)
Second misalignmen o he eed pla e (2)
Thi d misalignmen
o he eed pla e (3)
Lack o p oduc in he eede
while he DRR machine
is ope a ing (4)
Figu e 18. Mul iple accumula ed alues (boa s, emp y, doubles and small pa s).
This ype o g aph allows us o obse e i he calib a ion p ocess en ails a comp ehensi e
imp o emen in pe o mance o , con e sely, whe he an imp o emen in a pa ame e a e an
adjus men en ails a educ ion in goodness o i in ano he . In (1), (2) and (3) i can be obse ed how
he eeding pla e goes ou o adjus men a li le mo e each ime, which has an impac on he numbe
o doubles and small pa s. Du ing all o his ime, he g ow h in he numbe o “boa ” oli es is
mono onous which means ha he e a e no misalignmen s in he s aigh ening b ush. Fo i s pa ,
he numbe o emp y (4) inc eases ab up ly each ime a lack o p oduc in he eede when he DRR
machine is wo king. A imes, he da a analysis wi hou accumula ing da a can be in e es ing in o de
o see he e ec o speci ic ac ions. In Figu e 19, he peaks in he “boa ” oli es/doubles (1), o only
doubles (2), emp y (3) a e obse ed due o he ac ion o isola ed adjus men s o ope a ion o he
machine. In all o he cases shown, hese ac ions a e un a o able (ei he calib a ion o ope a ion).
(1) (2) (3) (2)
Figu e 19. Ins an aneous alues o boa s, doubles, small pa s and emp y.
Figu e 18. Mul iple accumula ed alues (boa s, emp y, doubles and small pa s).
This ype o g aph allows us o obse e i he calib a ion p ocess en ails a comp ehensi e
imp o emen in pe o mance o , con e sely, whe he an imp o emen in a pa ame e a e
an adjus men en ails a educ ion in goodness o i in ano he . In (1), (2) and (3) i can be obse ed
how he eeding pla e goes ou o adjus men a li le mo e each ime, which has an impac on he
numbe o doubles and small pa s. Du ing all o his ime, he g ow h in he numbe o “boa ” oli es
is mono onous which means ha he e a e no misalignmen s in he s aigh ening b ush. Fo i s pa ,
he numbe o emp y (4) inc eases ab up ly each ime a lack o p oduc in he eede when he DRR
machine is wo king. A imes, he da a analysis wi hou accumula ing da a can be in e es ing in
o de o see he e ec o speci ic ac ions. In Figu e 19, he peaks in he “boa ” oli es/doubles (1), o
only doubles (2), emp y (3) a e obse ed due o he ac ion o isola ed adjus men s o ope a ion o he
machine. In all o he cases shown, hese ac ions a e un a o able (ei he calib a ion o ope a ion).
Senso s 2020, 20, x FOR PEER REVIEW 18 o 22
Fi s misalignmen
o he eed pla e (1)
Second misalignmen o he eed pla e (2)
Thi d misalignmen
o he eed pla e (3)
Lack o p oduc in he eede
while he DRR machine
is ope a ing (4)
Figu e 18. Mul iple accumula ed alues (boa s, emp y, doubles and small pa s).
This ype o g aph allows us o obse e i he calib a ion p ocess en ails a comp ehensi e
imp o emen in pe o mance o , con e sely, whe he an imp o emen in a pa ame e a e an
adjus men en ails a educ ion in goodness o i in ano he . In (1), (2) and (3) i can be obse ed how
he eeding pla e goes ou o adjus men a li le mo e each ime, which has an impac on he numbe
o doubles and small pa s. Du ing all o his ime, he g ow h in he numbe o “boa ” oli es is
mono onous which means ha he e a e no misalignmen s in he s aigh ening b ush. Fo i s pa ,
he numbe o emp y (4) inc eases ab up ly each ime a lack o p oduc in he eede when he DRR
machine is wo king. A imes, he da a analysis wi hou accumula ing da a can be in e es ing in o de
o see he e ec o speci ic ac ions. In Figu e 19, he peaks in he “boa ” oli es/doubles (1), o only
doubles (2), emp y (3) a e obse ed due o he ac ion o isola ed adjus men s o ope a ion o he
machine. In all o he cases shown, hese ac ions a e un a o able (ei he calib a ion o ope a ion).
(1) (2) (3) (2)
Figu e 19. Ins an aneous alues o boa s, doubles, small pa s and emp y.
Figu e 19. Ins an aneous alues o boa s, doubles, small pa s and emp y.
Senso s 2020,20, 1541 19 o 22
Finally, Figu e 20 shows ins ances in which he pe o mance dec eases when wo king wi h
a machine wi h a poo ly adjus ed eeding pla e and s aigh ening b ush (1), only wi h he eed pla e
misaligned (2) o due o he lack o p oduc in he eede (3).
Senso s 2020, 20, x FOR PEER REVIEW 19 o 22
Finally, Figu e 20 shows ins ances in which he pe o mance dec eases when wo king wi h a
machine wi h a poo ly adjus ed eeding pla e and s aigh ening b ush (1), only wi h he eed pla e
misaligned (2) o due o he lack o p oduc in he eede (3).
(1)
(2)
(2)
(3)
Figu e 20. Ins an aneous alues o speed and p oduc ion.
4. Conclusions
Wi h he cu en pape , an IoT con ol sys em o a DRR machine has been de eloped which
allows i s ope a ion o be imp o ed:
An IoT sys em has been de eloped which allows o he ecep ion o use ul ope a ional
in o ma ion o he DRR machine emo ely in o de o do an ope a ional diagnosis based on his
in o ma ion, and, in his case, ca y ou calib a ion ope a ions. This IoT sys em allows o
con igu a ion o he equipmen ia a emo e desk op.
In he second place, he classi ica ion sys em has been made o wo k in an indus ial se ing a a
nominal speed o he DRR machine (up o 2500 oli es pe minu e) h ough he use o a PC-CM1K
connec ion which uses a mic ocon olle wi h an In el Cu ie o Teensy “ i ual po ” as an in e ace.
In he hi d place, he aining and es ing o a neu al ne wo k based on a physical chip o classi y
oli es in he eed chain o a pi ing machine has been success ully comple ed. I has been p o en ha
he use o In el Cu ie o CM1K chips a e sui able o simple so ing (no mal, emp y, boa s), wi h e o
a es o less han 5% wi h he In el Cu ie chip and less han 1% wi h he CM1K chip.
An applica ion de eloped in Q C ea o has been used ha p e iously de ec s o i s s udy he
cases wi h small pa s o oli es o double oli es p esen in he pocke and sepa a es hem om hose
ha a e sen o he neu al chip ha co espond o no mal, emp y pocke s and boa s.
One limi a ion o he sys em cu en ly de eloped is ha i only allows o he diagnosis o he
DRR machine so ha he ope a o can ecalib a e i . In a u u e pape , he possibili y o ejec ing hose
oli es which a e badly posi ioned (“boa ” oli es o doubles) o which ha e de ec s (small pieces) in
he eeding chain will be p esen ed. In his way, “boa ” oli es o doubles being badly de-s oned will
be a oided. Likewise, i will assu e ha he oli es which b eak upon en e ing in he eed chain
causing small pieces do no ge o he de-s oning a ea, hus a oiding, on he one hand, he loss o
p oduc (1–2%), and on he o he hand, he accele a ed de e io a ion o he punch needles and ha s
o deboning.
Au ho Con ibu ions: Concep ualiza ion, A.M.L.; alida ion, A.M.L., J.M.M.M., A.R.C., M.d.J.L., J.M.M.L.,
M.J.S. and A.L.P.; supe ision: A.M.L., J.M.M.M. and A.R.C.; in es iga ion, w i ing—o iginal d a p epa a ion,
w i ing— e iew and edi ing, concep ualiza ion, so wa e, o mal analysis, esou ces, da a cu a ion,
Figu e 20. Ins an aneous alues o speed and p oduc ion.
4. Conclusions
Wi h he cu en pape , an IoT con ol sys em o a DRR machine has been de eloped which
allows i s ope a ion o be imp o ed:
An IoT sys em has been de eloped which allows o he ecep ion o use ul ope a ional in o ma ion
o he DRR machine emo ely in o de o do an ope a ional diagnosis based on his in o ma ion, and,
in his case, ca y ou calib a ion ope a ions. This IoT sys em allows o con igu a ion o he equipmen
ia a emo e desk op.
In he second place, he classi ica ion sys em has been made o wo k in an indus ial se ing a
a nominal speed o he DRR machine (up o 2500 oli es pe minu e) h ough he use o a PC-CM1K
connec ion which uses a mic ocon olle wi h an In el Cu ie o Teensy “ i ual po ” as an in e ace.
In he hi d place, he aining and es ing o a neu al ne wo k based on a physical chip o classi y
oli es in he eed chain o a pi ing machine has been success ully comple ed. I has been p o en ha
he use o In el Cu ie o CM1K chips a e sui able o simple so ing (no mal, emp y, boa s), wi h e o
a es o less han 5% wi h he In el Cu ie chip and less han 1% wi h he CM1K chip.
An applica ion de eloped in Q C ea o has been used ha p e iously de ec s o i s s udy he
cases wi h small pa s o oli es o double oli es p esen in he pocke and sepa a es hem om hose
ha a e sen o he neu al chip ha co espond o no mal, emp y pocke s and boa s.
One limi a ion o he sys em cu en ly de eloped is ha i only allows o he diagnosis o he
DRR machine so ha he ope a o can ecalib a e i . In a u u e pape , he possibili y o ejec ing hose
oli es which a e badly posi ioned (“boa ” oli es o doubles) o which ha e de ec s (small pieces)
in he eeding chain will be p esen ed. In his way, “boa ” oli es o doubles being badly de-s oned
will be a oided. Likewise, i will assu e ha he oli es which b eak upon en e ing in he eed chain
causing small pieces do no ge o he de-s oning a ea, hus a oiding, on he one hand, he loss o
p oduc (1–2%), and on he o he hand, he accele a ed de e io a ion o he punch needles and ha s
o deboning.
Senso s 2020,20, 1541 20 o 22
Au ho Con ibu ions:
Concep ualiza ion, A.M.L.; alida ion, A.M.L., J.M.M.M., A.R.C., M.d.J.L., J.M.M.L.,
M.J.S. and A.L.P.; supe ision: A.M.L., J.M.M.M. and A.R.C.; in es iga ion, w i ing—o iginal d a p epa a ion,
w i ing— e iew and edi ing, concep ualiza ion, so wa e, o mal analysis, esou ces, da a cu a ion, isualiza ion,
p ojec adminis a ion: A.L.P. All au ho s ha e ead and ag eed o he published e sion o he manusc ip .
Funding: This esea ch ecei ed no ex e nal unding.
Con lic s o In e es : The au ho s decla e no con lic o in e es .
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