This document describes the development of a post-harvest quality evaluation system for mechanically harvested citrus fruit on a conveyor belt. The system uses machine vision to classify fruit as healthy, abnormal, or immature. It was tested on over 600 fruit across 5 trials, achieving over 90% accuracy on average for healthy fruit classification and 83% for abnormal fruit. Mass estimation using fruit diameter achieved an R2 of 0.96 and average error of 4.09 kg. The system provides a way to evaluate fruit quality and assess the impact of mechanical harvesting on future crops.

1. POST-HARVEST QUALITY EVALUATION
SYSTEM ON CONVEYOR BELT FOR
MECHANICALLY
HARVESTED CITRUS
Daeun Choi
Won Suk Lee
Reza Ehsani
Fritz Roka
Ce Yang

2. Contents
1 Motivation
2 Material and Method
3 Result
2
4 Conclusion

3. Contents
1 Motivation
2 Material and Method
3 Result
3
4 Conclusion

4. Motivation
4
 Post-harvest citrus quality evaluation system

5. Machine Vision
 Replace human workforce
 Non-destructive evaluation
 Fast, accurate and reducing cost
V
Vision sensor
5
Image adopted from Shin, J., W. S. Lee, and R. Ehsani. 2012.
Postharvest citrus mass and size estimation using logistic
classification model and watershed algorithm. Biosystems
Engineering 113(1): 42-53

6. Previous Works
 Blasco, J., N. Aleixos, and E. Molto. 2006. Computer vision detection
of peel defects in citrus by means of a region oriented segmentation
algorithm. Journal of Food Engineering 81: 535-543.
Region-oriented segmentation algorithm for detecting the most common peel
defects of citrus fruits
 Kohno, Y., N. Kondo, M. Iida, M. Kurita, T. Shiigi, Y. Ogawa, T. Kaichi
and S. Okamoto. 2011. Development of a Mobile Grading Machine for
Citrus Fruit, Engineering in Agriculture, Environment and Food
4(1):7-11.
Ripeness and diameter estimation using RGB color images
 Cubero, S., N. Aleixos, F. Albert, A. Torregrosa, C. Ortiz, O. Garcia-
Navarrete and J. Blasco. 2013. Optimised computer vision system
for automatic pre-grading of citrus fruit in the field using a mobile
platform. Precision Agric. 15: 80-94
Size and color index estimation using Lab color space
6

7. Motivation
 Mechanical harvester- canopy shaker
7
Tree

8. Challenges of Mechanical
Harvesting
 Valencia – One of major variety of citrus
8
Fruit for
following year Fruit for
harvesting

9. Challenges of Mechanical
Harvesting
9
Mechanical harvester using physical force to harvest citrus
Unavoidable removal of green citrus for following year
(Valencia)

10. Motivation
10

11. Objective
11
Develop a fruit inspection system for harvested
citrus fruit on a conveyor belt to identify
healthy, abnormal and small immature fruit for
following year.

12. Contents
1 Motivation
2 Material and Method
3 Result
12
4 Conclusion

13.  Conveyor belt system - driven by a tractor
pump
 Rotating speed - 48 cm/s
Hardware System
Prototype system for
inspecting citrus on a
conveyor belt
13

14. Imaging System
 GigE RGB camera (SXG10C, Baumer)
 ½” CCD, 1024*1024
 Shutter speed: 3 ms
 Focal length: 6 mm
 FOV: 80 cm by 60 cm
 Image acquired in every
1.5 sec
14
Inside of imaging box

15. Image used for experiment
 Typical image used for experiment
 Total 5 trials (average 18 images per trial)
 1 image including 10-30 fruit
15

16. Immature
Types of Citrus Fruit
16
Healthy
Abnormal
Total of 91 images including 617 healthy,
283 abnormal and 237 immature citrus

17. Machine Vision Algorithm
17
Color
correction
Background
removal
Circular Hough
transform
(CHT)
Classification
Mass
estimation

18. Color Correction
R* = R*0.7367 + 37.72
G* = G*0.6194 + 27.82
B* = B*0.6459 + 27.33
18

19. Background Removal
R Threshold = 50
G Threshold = 50
19
 Three classes for histogram analysis
 Mature: healthy, abnormal
 Green: small young fruit for following year
 Background: conveyor belt

20. Background Removal
20
Original image After thresholding

21. Finding Potential Areas
 Remained background, branches, leaves
 Most distinctive feature of citrus: circular shape
Circular Hough Transform
- finding circular object
- Input: image,
diameters
21

22. Finding Potential Areas
22
Compare
diameters and
center position
Circular Hough
Transform
Removing
falsely detected
circles

23. • Ensemble learning
multiple weak learners are trained
and combine their outputs to have
better accuracy.
 Random Forest: revised bagging
method with decision tree for
better generalization.
 Given a training set of size n, draw a sample
of size n*<n (resampling) and
For i = 1:M
Train classifier Ci, choose a different subset
of features randomly at each split.
end
Final classifier is a majority vote of C1 .. CM
Random Forest Classifier
23

24. Random Forest Classifier
• Features
 Color information
- Histogram in R, G, B, H, S, V, Y, Cb, Cr, L, a, b and
gray-scale
- Mean, standard deviation
 Size information: area, diameter
 Texture
- GLCM: contrast, energy, homogeneity, correlation
- 4 directions: minimum, maximum and average
24

25. Mass estimation
 Diameter in cm and in pixels comparison
 Regression analysis between mass, diameters
 400 oranges (350 mature, 50 immature)
Mass = 0.09*Diameter2
-4.90*Diameter + 80.97
R2 = 0.96
25

26. Contents
1 Motivation
2 Material and Method
3 Result
26
4 Conclusion

27. Image Analysis Result
Trial
No.
Healthy
Oranges
Abnormal
Oranges
Immature Oranges
for Following Year
Leaf &
Conveyor belt
1
Total 88 26 43 248
Correct
Identification (%)
86 (97.7) 22 (84.6) 38 (88.4) 246 (99.2)
2
Total 104 35 22 87
Correct
Identification (%)
97 (93.3) 29 (82.9) 18 (81.8) 78 (89.7)
3
Total 188 91 71 199
Correct
Identification
(%)
176 (93.6) 79 (86.8) 64 (90.1) 181 (91.0)
4
Total 138 76 35 233
Correct
Identification (%)
129 (93.5) 65 (85.5) 32 (91.4) 218 (93.6)
5
Total 99 55 66 180
Correct
Identification (%)
91 (91.9) 42 (76.4) 55 (83.3) 167 (92.7)
Average
Total 123.4 56.6 47.4 189.4
Correct
Identification (%)
115.8
(94.0)
47.4 (83.2) 41.4 (87.0) 178 (93.2)
27
Correctly Identified (%) = 100*Correctly identified object by the algorithm/Ground truth
The number of fruit correctly identified by the algorithm along with
the number of actual fruit by manual counting

28. Mass Estimation Result
28
Mass estimation results of each trial by the algorithm (kg)
Trial No. Actual Mass Estimated Mass R2
RMSE
1 22.4 27.6
2 32.9 33.7
3 50.2 58.13 0.96 4.09
4 43.8 48.0
5 36.4 38.8
Average 37.1 41.2

29. Contents
1 Motivation
2 Material and Method
3 Result
29
4 Conclusion

30. Conclusion
1. A prototype system for fruit inspection of
harvested citrus fruit on a conveyor belt was
developed.
2. Overall classification result was good for
healthy fruit.
3. Irregular patterns of abnormal citrus caused
to lower prediction ability of the classifier.
4. Identifying immature fruit on the conveyor
belt can be used to find effect of mechanical
harvesters for following year crops.
5. Distinguishing healthy and abnormal fruit can
be used for quality evaluation of harvested fruit.
30