POST-HARVEST SORTING OF FRUITS BY OPTICAL GRADERS

Efficiency tests on citrus fruits and table tomatoes

Università degli Studi ………Prof. Mario ROSSI

INTERNATIONAL SYMPOSIUM ON FRESH PRODUCE SUPPLY CHAIN MANAGEMENT

6-10 December 2006, Lotus Pang Suan Kaeo Hotel, Chiang Mai, Thailand

Italian fruits and vegetables have lost ground in terms of competitiveness…• On both domestic

and international markets

Why:•Extreme fragmentation of the business fabric•Extreme fragmentation of distribution channels•Delayed enforcement of UE quality regulations•High “quality” of imported produce

Therefore producers should put a greater emphasis on post-harvest operations

• Correctly performed and optimized post- harvest operations result in a remarkable increase in produce quality

• What is more, post-harvest technology is continuously developing

Fuits and Vegetables Sorting LineGeneral Features

LINE FEEDING

MANUAL SORTING

WASHING AND DRYING

WAXING-TREATMENTS

GRADING

SORTING

MANUAL SORTING

PACKING

Fruits and Vegetables Ranking

• Ranking essentially relies on: size, weight, colour (extrinsic features)

• Today sophisticated ranking systems are being developed to assess also fruit intrinsic features (pulp firmness, degree of maturation, chemical characteristics, etc..)

*Few experimental data are available about the actual operational parameters of the different systems used to carry out the most important of post-harvest operation: grading

GRADING,Under EU Fruits and Vegetables Commercialization Regulations: grading is determined by the measurement of maximum chord of the normal section of the fruit polar axis.

In fruits and vegetables packhouses grading is performed by continuosly evolving special systems.

Mechanical systems are still utilized which are based on elements including:

Vibrating meshes with gauged holes Properly distanced tilting rollers

However mechanical systems are poorly accurate….

Diverging Conveyors System

Do not allow for a fast change in sorting schemes according to the different fruits to be sorted;

Need a lot of labour to make up for grading errors;

Have a limited operation capacity and a remarkable overall dimension.

Are losing ground to electronic systems.

Electronic graders are in most cases based on vision technology: a digital image processor analyzes fruit images acquired by one or more videocameras to obtain the geometric sizes required for correct grading.

Operation of Optical GradersCopiare

• I sistemi di movimentazione portano i frutti all'interno di una camera di visione, dove una telecamera acquisisce le immagini del prodotto che transita.

• Nel momento in cui i frutti entrano nella camera di visione i doppi coni che li supportano cominciano a ruotare, provocando anche la rotazione dei frutti stessi, indispensabile per permettere la visione di tutta la loro superficie. Avviene quindi l'acquisizione di più immagini per ogni frutto, che vengono inviate ad una scheda di acquisizione con convertitore analogico-digitale e riversate in un calcolatore.

• Identificati i parametri dimensionali del frutto, il calcolatore lo destina alla corrispondente uscita, seguendo il programma di selezione impostato dall'operatore. In corrispondenza dell'uscita impostata per la classe cui il frutto appartiene, parte l'impulso che aziona il sistema di espulsione ed il prodotto abbandona il nastro di movimentazione per confluire nei bancali di lavorazione, dove operatori qualificati provvedono alle operazioni di confezionamento

Optical graders are charaterized by:•elevated accuracy;•the possibility of changing sorting schemes;•gentle handling of fruits.

• Many are the optical grader models produced today which are likely to meet the different needs of the most diverse businesses. Producers declare a grading accuracy within 1 mm also at a conveyor speed of 11 fruits/sec.

• But is this speed real for all fruits and at all speeds?

• Therefore it was decided to study the operation of two optical graders operating in two modern plants in order to obtain data on their efficiency on the basis of some preliminary tests performed with oranges, lemons and table tomatoes

The study focused on the operation features and on the yield quality of two plants: A and B

(*) the plant is made up of two identical and independent sorting lines

Plant Producer Model Lanes (n.)Drop

LocationsSorting Device

A MaxfrutMaxsorter

v 20004 5 Videocamera

B* UnitecUnical

600T–OC6 30 Videocamera

TEST METHODOLOGIES

The operation capacity of the two graders under study was measured by assessing the filling percentage of those sectors of the roller conveyors destined to held fruits and by applying the following relation:

C = V x 3600 x R x n [fruits/ h], where:

C = operation capacity;

V = speed, as expressed in the sectors/s (= fruits/s, = 0.1 m/s);

R = percentage of sectors filled;

n = number of lanes.

TESTS METHODOLOGIES Tests were made to determine the accuracy of grading in the lines under consideration.

Plant produceFruit/test

(n.)

Gauge

(mm)

Speeds

(n.)Repeats

(n.)Observations

(n.)

A oranges 20 50-104 3 10 600

B lemons 15 50-84 2 10 300

B tomatoes 15 48-85 2 10 300

TESTS METHODOLOGIES • Fruits were numbered with a manual

gauge.

• They were then placed, one by one, on the conveyors of Graders A and B and advanced to pass thruogh a vision chamber.

• Ten repeats were made for each fruit.

• Tests were repeated at three speeds of the conveyor for A: 0.6 m/s, 0.8 m/s and 1.1 m/s; and at two speeds for B: 0.8 m/s e 1.1 m/s.

• Data from the two graders were compared with the measurements made with the manual gauge.

RESULTS AND REMARKS

Operation capacity of the graders under study

GraderLanes(n°)

Fruitsprocessed

Speed(m/s)

Operation capacity(fruits/h)

Operation capacity

(t/h)

Workers(n°)

A 4 oranges0.8 80,640 6.77

13-161.1 11,0880 9.31

B* 6 lemons0.8 12,0960 9.67

20-301.1 16,6320 13.30

B* 6 Tomatoes0.8 12,2688 9.69

20-301.1 168,696 13.32

* Data of one of the two graders of Plant B

Graders A and B where found to have a filling percentage of the conveyor sectors destined to fruits of about 70% which remained constant at different operation speeds.

Operation capacity is extremely elevated.

RESULTS AND REMARKS

Grading accuracy tests

Data from the two graders were compared with the measurements made with the manual gauge.

Data were analyzed taking into account the absolute error (Ae), i.e. the difference between the fruit "equatorial" diameter calculated by the grader and that measured by the manual gauge.

Ae = Cm – Cr [mm]where:• Cm = maximum chord of the

section normal to the friut longitudinal axis measued by the optical device;

• Cr = maximum chord of the section normal to the friut longitudinal axis measued by the manual gauge.

The influence on Ae of both fruits speed and gauge was assessed.

RESULTS AND REMARKS

Grader A

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

50 66 66 66 66 71 71 72 72 72 74 76 77 77 81 85 87 91 95 104

"Maximum equatorial diameter" of the fruits (mm)

Ave

rage

ab

solu

te e

rror

(m

m)

v = 0.6 m/s v = 0.8 m/s v = 1.1 m/s

Machine A - Absolute Error (average of 10 repeats) registered for sample fruits (oranges)

- Absolute error does not tend to increase at higher speeds;

- Underestimate error at least for smaller frits

RESULTS AND REMARKS

Machine B - Absolute Error (average of 10 repeats) registered for sample fruits (lemons)

- Modest absolute error; it does not increase at higher speeds;

- It tends to increase in larger fruits

Grader B

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

50 50 59 59 65 66 68 68 73 77 78 81 81 82 84

"Maximum equatorial diameter" of the fruits (mm)

Ave

rage

ab

solu

te e

rror

(m

m)

v = 0.8 m/s

v = 1.1 m/s

RESULTS AND REMARKS

Grader A, B - Correlation between fruit gauge and the absolute error (Ae) of the machine

measurement (grader A- oranges; grader B – tomatoes)

- A significant correlation is observed between increase in fruit size and increase in absolute error;

- Larger fruits are more irregularly shaped and, sometimes, do not rotate inside the vision chamber, thus preventing the acquisition of images of the entire suface of fruits.

Grader B

r = - 0.560 P ≤ 0.05

-4

-2

0

2

4

45 55 65 75 85 95

Average maximum equatorial diameter (mm)

Average Ae (mm)

Grader A

r = 0.636 P ≤ 0.01

-4

-2

0

2

4

6

40 50 60 70 80 90 100 110

Average maximum equatorial diameter (mm)

Average Ae (mm)

RESULTS AND REMARKS

Average standard deviation and variation coefficient (C.V.) for graders A and B

- Test repeats highlighted a good level of homogeneity in the diameter ”readings" made on the same samples during successive passings before the videocamera, as can be inferred by the standard deviation average and the variation coefficient given in the Table below.

Speed Grader A Grader B Grader B

(m/s) St. Dev. V.C. St. Dev. V.C. St. Dev. V.C.

0.6 0.86 1.14 lemons tomatoes

0.8 0.96 1.25 0.86 1.22 0.82 1.21

1.1 121 1.60 0.76 1.09 0.80 1.23

RESULTS AND REMARKS

CopiareLe norme comunitarie in materia di commercializzazione degli agrumi e del pomodoro precisano che, per tutte le categorie e per ogni forma di presentazione, è ammesso solo il 10 % in peso o in numero di frutti che abbiano il calibro immediatamente inferiore e/o superiore a quello indicato sulla confezione.Pertanto si è ritenuto opportuno prendere in considerazione l'errore assoluto in valore assoluto (|Ae|) delle rilevazioni effettuate sui frutti dalle due calibratrici, rispetto al calibro reale. Sono stati stabiliti tre intervalli di errore e si è voluto indicare la percentuale di casi, rilevati durante le prove alle differenti velocità, che ricadono in essi.

RESULTS AND REMARKS

CopiarePer la calibratrice A, al lavoro su arance, si è registrato un errore massimo, in valore

assoluto, di 7 mm.Considerando l'intervallo di errore assoluto compreso tra 5 e 7 mm, si osserva che risultano affette da un errore di tale entità l'uno per cento delle misure alle velocità di 0.6 e 1.1 m/s. Alla velocità di 0.8 m/s non si supera il tre per cento di casi con tale errore.

57%42%

1%

53%44%

3%

49%50%

1%

v = 0.8 m/s v = 1.1 m/sv = 0.6 m/s

■ 0 │Ae│2 ■ 2 │Ae│5 ■ 5 │Ae│7

Grader A - oranges: percentage of cases observed on three intervals of absolute error in terms of absolute value (mm) |Ae|

RESULTS AND REMARKS

CopiareI risultati ottenuti con la calibratrice B evidenziano una precisione più elevata, in quanto l'errore assoluto massimo è stato di 5 mm alla velocità di 0.8 m/s e di soli 4 mm alla velocità di 1.1 m/s.

Inoltre più dell'80% dei frutti sono stati misurati con un errore assoluto in valore assoluto inferiore ai 2 mm, mantenutosi inferiore o uguale ad 1 mm nella maggior parte dei casi.

Grader B – lemons and tomatoes: percentage of cases observed on three intervals of absolute error in terms of absolute value (mm) |Ae|

82%

18%

81 %

19%

v = 0.8 m/s

■ 0 │ Ae│2

■ 2 │ Ae│5

v = 1.1 m/s

■ 0 │ Ae│2

■ 2 │ Ae│4

CONCLUSIONS

• Copiare•Gli errori registrati sono superiori a quelli generalmente dichiarati dalle case costruttrici; tuttavia, anche quest'ultima elaborazione evidenzia che il lavoro di calibratura viene svolto accuratamente a tutte le velocità di prova ed in particolar modo dalla calibratrice B.

• In entrambi i casi, i dati ottenuti attestano livelli di precisione soddisfacenti, soprattutto considerando che, volutamente, sono state condotte prove "difficili" per le macchine, scegliendo frutti-campione di dimensioni e forme differenti ed operando anche a velocità superiori a quelle comunemente adottate durante le lavorazioni e consigliate dalle case costruttrici.

• L’analisi del lavoro condotta sulle selezionatrici ottiche ha permesso di evidenziare che tali macchine, pur lavorando a velocità elevate, che garantiscono una grande capacità di lavoro, mantengono un’elevata precisione nell’operazione di selezione in base al calibro.

CONCLUSIONS

CopiareLa precisione consente il risparmio di manodopera alle uscite, in quanto gli addetti di tale settore devono semplicemente trasferire i frutti nei contenitori appositi, mentre, negli impianti tradizionali, a volte, erano necessari un ultimo controllo e comunque una maggiore attenzione, alle uscite, per compensare gli eventuali errori di calibratura.

CONCLUSIONS

The machines under study are in addition characterized by the possibility of varying the grading schemes selected by simply acting on the control panel. They are also able to minimize fruit damage thus responding to the demand of quality on the part of consumers. Accuracy and the possibility of processing different types of fruits result in a prolonged use of the graders throughout the year thus reducing both labour and post-harvest operation costs.

In order to obtain better results optical graders need careful upkeep and calibration as well as well skilled workers trained to manage at best the control systems of their optical/electronic devices.

The selection of graders must be carefully weighed based on their own technical specifications and on the needs of their users.

POST-HARVEST SORTING OF FRUITS BY OPTICAL GRADERS

Efficiency tests on citrus fruits and table tomatoes

Università degli Studi Mediterranea di Reggio Calabria

Prof. Gennaro GIAMETTA

INTERNATIONAL SYMPOSIUM ON FRESH PRODUCE SUPPLY CHAIN MANAGEMENT

6-10 December 2006, Lotus Pang Suan Kaeo Hotel, Chiang Mai, Thailand