The final colloquium of my Dissertation, in the context of my MSc. studies in the University of Wageningen, in the Netherlands.

1. Final MSc Thesis Presentation
Evaluation of harvesting and packaging operations in a
Greek tomato-production greenhouse, with the use of
model-based method
21/1/2016, Ioannis Moutsinas

2. Contents
 Introduction
 Materials & Methods
 Results
 Discussion
 Conclusions
 Recommendations
2

3. Introduction
Agritex company
3

4. Harvesting and packaging
 Harvesting: sorting of trusses according to number of
red tomatoes
• Simple sorting: in two classes (Lucia and
standard); in carbon boxes (6.5–7kg); separate
packaging in processing room; several products
• Intensive sorting (packaging): two classes –up to
six classifications + box weight calibration in
processing room; in carbon boxes (5.5 - 7kg)
 Criteria:
● Harvest type: if expected yield > 50 tons 
combined harvest & packaging in path and vice
versa
● Classification: size, shape, colour
4

5. 5

6. 6

7. Problem description
 High labour costs + unstable labour efficiency + limited
trained personnel  use personnel optimally
 First step: simulate labour processes  re-design
 This study: Packaging in Path (PinP) (sub)model
 Objective: evaluate performance and efficiency of
alternative (work) methods for the in-path packaging
operation using a model-based method
7

8. Research questions
1. Is packaging in path (PinP) sub-model able to represent the
harvesting and packaging operations in a Greek tomato
greenhouse?
2. What is the performance of the harvesting and packaging
operations in path level, within the Agritex Company for the
years 2012, 2013?
3. What is the best work method to harvest and package Idooll
tomato cultivar in a (Greek) greenhouse?
8

9. Materials & Methods
Problem approach
In-path packaging methods of typical cultivar (Idooll)
IDEF3 analysis
Behaviour and labour registration data analyses
Define and simulate list of work scenarios
9

10. Idooll cultivar
 Tomato weight: 140-150g
 Diameter ≈ 60 mm
 Tomatoes per truss: 3-5
 Rows (2013): 150-314 (both)
10
Quality
classes
Name Capacity (kg)
1 Lucia Big Red 5.5
2 Lucia Small Red 5.5
3 Lucia Big Semi-Red 5.5
4 Lucia Small Semi-Red 5.5
5 Blue Pair 6.5 – 7
6 Blue Single 6.5 – 7

11. Data processing
1. Videos & scripts of harvesting and packaging in
greenhouse (grower)
2. Labour registration data (Nomad)
1. IDEF3 process analysis (AllFusion process modeller) &
Behaviour analysis (Noldus Observer XT)
2. Descriptive statistical analysis 2012 (Excel)
11

12. IDEF3 process analysis
 First pre-modelling step
 Method to record network of relevant actions in a
process within a context of operation(s)
 Raw data: video and scripts by grower
 Goal: Record combined harvest and packaging in-path
operation + Weight calibration process in packaging
room  model structure
12

13. Data analysis
 General (all cultivars) and specific (only Idooll) harvest
analyses
● Trace and assess yield effects on harvesting 
Simulation dates
● Relations between harvested yield and harvest rate
or harvest duration per path  input and
verification data
Data filtering
 Behaviour analysis
● Coding scheme of movements + videos observation
 Probability distributions of basic processes (cut,
prune, allocate)
13

14. PinP submodel
14
 Discrete event system: System’s state transition ruled by
asynchronous discrete incidents (events); environment:
entities, attributes, events, resources, queues
 represents the Harvest & Packing process (Idooll) at path
level + the weight calibration in the processing room
 Entities (resources): harvester, trolley, truss, box
 Matlab, Simulink environment, SimEvents toolbox

15. Model structure
15
Inputs
Greenhouse dimensions
Production system dimensions
Plant density
Daily yield
Daily path schedule
Probability density functions
Run settings
Initial state
Velocity vectors of operators
PinP model
Harvest & packaging
processes
Reports
Outputs
Job cycle times
Product throughput
Labour times
Transportation times
Performance parameters
 Subsystems:
1) Harvest and package in path (harvester, trolley)
2) Transportation (tractor driver)
3) Processing room (worker)

16. Simulation scenarios
 Aim: test model’s functionality and flexibility + find most
effective work method
 Simulation of single harvest session: single (average)
harvester, trolley, tractor driver, worker, group of paths
 Simulation date: May 20th, 2013
 Comparison on yield and time related parameters
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Scenario Description Packaging
Box
Classifications
Weight
calibration in PR
S0 Reference Manual 6 Yes
S1 Simplified sorting method Manual 2 Yes
S2 Automated packing in path Automated 6 Yes
S3 Complete H&P in path Manual 6 No

17. Model calibration & validation
 Reference scenario
 Dates: May 20th, March 20th, April 14th, April 24th
 Complete harvest single path by avg. harvester-trolley
 Measured vs simulation data
● Measured data: daily average times for a path;
optional data filtering applied (outliers)
17
Performance indicator Units
Yield per path kg
Yield per path (boxes) -
Time to harvest a path minutes
Harvest rate kg min-1
Box filling rate min box-1
Average box weight kg
Truss quality ratio -

18. Results
Model calibration
18
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Yield per
path (kg)
Yield per
path (boxes)
Harvest time
per path
(min)
Harvest rate
(kg min-1)
Box filling
rate (min
box-1)
Average box
weight (kg)
Truss quality
ratio (-)
Accuracy(-)
Performance parameter
Initial Calibrated Ideal

19. Model validation
19
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
3/20/2013 4/12/2013 4/24/2013
Harvestrate(kgh-1)
Dates
0
10
20
30
40
50
60
3/20/2013 4/12/2013 4/24/2013
Harvesttime(min)
Dates
0
50
100
150
200
3/20/2013 4/12/2013 4/24/2013
Harvestedyield(kg)
Dates
measured
simulated
0
0.5
1
1.5
2
2.5
3/20/2013 4/12/2013 4/24/2013
Timeperbox(min)
Dates

20. Scenarios comparison
20
Performance parameter Value
S0 S1 (%) S2 (%) S3 (%)
Harvested yield (kg) 208.6 203.3 (-2.6) 210 (+0.7) 209 (+0.04)
Yield of Lucia tomatoes (kg) 178 174.5 (-2) 179 (+0.6) 160 (-11.3)
Yield of Blue tomatoes (kg) 30 28.8 (-4.2) 30.4 (+1.3) 49 (+63.3)
Filled boxes (-) 34 33 (-3) 34 35 (+2.9)
Lucia boxes (-) 31 30 (-3.3) 31 28 (-10.7)
Blue boxes (-) 3 3 3 7 (+133)

21. 21
Time parameter Value (sec)
S0 S1 (%) S2 (%) S3 (%)
Total harvesting duration 3188 3163 (-0.8) 2406 (-32.5) 3963 (+24.3)
Mean time to harvest truss 2.34 2.29 (-2.1) 2.33 (-0.4) 2.55 (+8.9)
Time interval for truss harvest 8.86 (0.984) 8.93 (+0.8) 6.68 (-32.6) 12.4 (+39.9)
Net packing duration 647.6 761.5 (+17.5) 0 1672 (+158)
Mean time to pack truss 1.9 (0.02) 2.15 (+13.1) 0 5.21 (+174)
Time to fill a trolley 3504 3341 (-4.8) 2548 (-37.5) 4314 (+23.1)
Time to sort trusses NA 1334 NA NA
Time to calibrate boxes’ weight 464 466 (+0.4) 429 (-8.2) NA

22. Discussion
 Despite limited parameter comparison, model showed
good adaptation to reality
 Despite limited applicability, alternative scenarios
simulation results showed good model adaptation
 Overall quality of labour data can be characterized as
average but not unreliable
● Relation equations not reliable for use
 Inter-planting did not affect simulation results
 S1: increased time in processing room (+22 min)
 S2: decreased time in path (-32%)
 S3: increased time in path (+24%)
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23. Conclusions
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1. Model: Good adaptation to reality
● 85-115%; 85-123%
● Flexible to yield changes  time changes (avg.
worker)
2. Harvest demand dependent on crop productivity (yield)
 two peaks: February & July (two cultivation periods)
● Harvest rate and time proportional to yield
● Harvest time per truss reversed proportional to
yield
● Coefficient of determination (R2) not high enough
3. Best scenarios: S2, S0, S3, S1
● Significant variations in time parameters:
(+29.5%; -25%; +8.7%)
● Yield variations not significant

24. Recommendations
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 Model performance:
● Expand model for other cultivars
● Incorporate with GWorkS model
● Improve behavior analysis with more video footage
● Simulate multiple trolley visits in a path
● Introduce alternative scenarios for tomato processing
 Grower:
Apply reference scenario whenever possible
Evaluate the possibility to introduce automation in
path
Educate and motivate workers to use Nomad
Expand Nomad’s database

25. Thank you!
Questions?
25

26. Agritex cultivation practises
 Crop replacement process
● Regular: complete crop removal and installation of
new separately for the two greenhouse sections (2
cultivation periods); applied in 2012 and before
● Inter-planting: gradual new crop integration in
February; tested in paths 310-614; applied in 2013
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27. Combined harvesting and packaging in path
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28. Package subsystem
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29. Harvest analysis (2012)
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30. Idooll harvest analysis (2012)
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31. Results
Behaviour analysis
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Action pdf type μ σ n Behaviour* μ* σ* n* Distribution*
Cut truss Gaussian 1.69 0.65 38 Cut truss -0.17 0.52 1368 Lognormal
Prune truss Gaussian 2.28 1.11 40 Remove unwanted parts -0.19 0.68 952 Lognormal
Allocate truss Gaussian 3.0 1.26 39 Place truss in box -0.21 0.77 1355 Lognormal
Cut loose tomato Gaussian 0.6 0.196 40 -
Check for next truss Gaussian 1.37 0.9 19 Move to next truss -0.01 0.58 1431 Lognormal
Bring a new box - 6.37 2 3
Place brand stickers - 9.22 2.92 8
Place paperboards - 4.36 1.23 4
Fix box uniformity - 8.17 6.20 5
Other - 1.75 0.59 2

32. Validation trials results
32
20/03/2013 12/04/2013 24/04/2013
Measured Simulated Accuracy Measured Simulated Accuracy Measured Simulated Accuracy
Harvested yield
(kg)
127.99 135.2 1.06 172.5 173.05 1.01 117 119 1.02
Filled boxes (-) 23 19 0.82 31 26 0.83 21 17 0.81
Harvest rate
(kg/min)
3.12 3.48 1.12 3.40 3.89 1.15 3.44 3.63 1.05
Harvest time
(min)
41.0 38.8 0.95 50.7 44.4 0.88 34 32.8 0.96
Time per box
(min/box)
1.76 2.04 1.16 1.62 1.71 1.06 1.62 1.93 1.19
Average box
weight (kg)
5.75 7.1 1.23 5.75 6.65 1.16 5.75 7.0 1.21

33. Model calibration results
33
Performance indicator Measured Initial Simulated Accuracy Calibrated Accuracy
Yield per path (kg) 171 178 1.04 174 1.02
Yield per path (boxes) 30 25 0.84 26 0.87
Harvest time per path (min) 47 52 1.10 45 0.96
Harvest rate (kg min-1) 3.63 3.42 0.94 3.88 1.03
Box filling rate (min box-1) 1.57 2.08 1.32 1.73 1.10
Average box weight (kg) 5.75 7.36 1.28 6.65 1.15
Truss quality ratio (-) 0.20 0.3 1.50 0.17 0.85

34. Calibration results
34
Time parameter Value (sec)
Initial Difference (%) Calibrated
Total harvesting duration 3132 +1.8 3188
Net harvesting duration 906.5 -6.8 844.7
Mean harvest time per truss (std. error) 2.7 (0.054) -15.3 2.34 (0.0062)
Time interval for truss harvest (std. error) 9.40 (0.555) -6.1 8.86 (0.984)
Min – max of time interval 2.17 - 130.4 - 2.39 – 340.7
Net packing duration 867 -33.8 647.6
Mean time to pack truss (std. error) 2.6 (0.0136) -12.1 2.32 (0.0196)
Time to bring new boxes 174 +86.8 325
Time to fill a trolley 3277 +6.9 3504
Time to calibrate boxes’ weight 434 +6.9 464

35. Calibrated parameters
 Mean & std. of probability distribution of the number of
tomatoes per truss decreased  reduce total path yield
 Std. of probability distribution of tomato colour reduced
 increase Lucia trusses
 Lucia box capacity reduced  increase number of boxes
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