Fruits quality evaluation In developing country, the grading of fruits are almost handled by human. That is, neverthelessm, time-consuming process and easily inconsistent. Therefore, researchers in this field has developed various algorithms for evaluating quality using computer vision and machine learning. In this research project, I have also proposed a method using image processing technique to inspect quality of fruits involves image segmentation and feature extraction step.
I would like to express my special thanks of gratitude to my tutor (Tran Tien Duc ), who is eaching at HCMC University of Technology and Education. He helped me a lot in doing this research and also is one of my loved teacher I've ever met and worked together.
Dataset of fruits were obtained from COFILAB team.
Segmentation and contour detection processes
Process
Mango Apple golden
Gray
Blur
Inrange
Canny
Dilate
FloodFill
Remove noise
Contour
Area
Dimension
Isolation
Feature extraction
File name
Area measured by image analysis (number of pixels)
Width measured by image analysis
Height measured by image analysis
Mass measured by weighting (g)
Status
Mango_01_B.JPG
267919
637
556
436.78
Maturity
Mango_02_B.JPG
314896
694
586
511.79
Maturity
Mango_03_B.JPG
294155
646
561
444.76
Maturity
Mango_04_B.JPG
311824
694
567
459.47
Maturity
Mango_05_B.JPG
299544
676
591
457.67
Maturity
Mango_06_B.JPG
296080
699
533
481.72
Maturity
Mango_07_B.JPG
313918
712
576
474.54
Maturity
Mango_08_B.JPG
301669
669
572
473.52
Maturity
Mango_09_B.JPG
315649
697
600
479.97
Maturity
Mango_10_B.JPG
302939
679
599
478.47
Maturity
Mango_11_B.JPG
321777
706
586
477.33
Maturity
Mango_12_B.JPG
285883
655
562
455.42
Maturity
Mango_13_B.JPG
315587
682
595
487.60
Maturity
Mango_14_B.JPG
295082
653
599
472.67
Maturity
Mango_15_B.JPG
293621
662
576
480.83
Maturity
Mango_16_B.JPG
291285
672
543
440.62
Maturity
Mango_17_B.JPG
314757
675
607
524.13
Maturity
Mango_18_B.JPG
300686
684
578
472.50
Maturity
Mango_19_B.JPG
303050
673
589
508.37
Maturity
Mango_20_B.JPG
333246
720
622
516.80
Maturity
Mango_21_B.JPG
323454
701
617
458.57
Maturity
Mango_22_B.JPG
310483
648
597
590.16
Maturity
Mango_23_B.JPG
294239
658
597
545.44
Maturity
Mango_24_B.JPG
289883
680
569
458.00
Maturity
Mango_25_B.JPG
296397
657
591
463.54
Immaturity
Mango_26_B.JPG
277712
665
545
401.21
Immaturity
Mango_27_B.JPG
287156
643
586
437.34
Immaturity
Mango_28_B.JPG
325324
703
613
532.63
Immaturity
Mango_29_B.JPG
285720
659
566
437.36
Immaturity
Mango_30_B.JPG
285922
674
548
417.73
Immaturity
Mango_31_B.JPG
303157
655
612
494.06
Immaturity
Mango_32_B.JPG
314016
678
608
513.60
Immaturity
Mango_33_B.JPG
304348
707
575
477.24
Immaturity
Mango_34_B.JPG
321335
709
607
508.97
Immaturity
Mango_35_B.JPG
301064
704
580
464.89
Immaturity
Mango_36_B.JPG
293339
710
545
427.72
Immaturity
Mango_37_B.JPG
381422
775
634
640.44
Immaturity
Mango_38_B.JPG
309734
709
576
492.18
Immaturity
Mango_39_B.JPG
411628
834
654
739.40
Immaturity
Mango_40_B.JPG
420399
845
661
757.72
Immaturity
Mango_41_B.JPG
375253
773
629
658.54
Immaturity
Mango_42_B.JPG
360528
748
603
620.49
Immaturity
Mango_43_B.JPG
345198
728
645
592.82
Immaturity
Mango_44_B.JPG
335209
718
612
557.35
Immaturity
Mango_45_B.JPG
312361
752
544
484.43
Immaturity
Mango_46_B.JPG
401422
800
649
663.17
Immaturity
Mango_47_B.JPG
352250
734
614
600.40
Immaturity
Mango_48_B.JPG
273928
664
553
505.75
Immaturity
Mango_49_B.JPG
348721
746
615
458.05
Immaturity
Mango_50_B.JPG
310015
677
585
541.46
Immaturity
File name
Area measured by image analysis (number of pixels)
Width measured by image analysis
Height measured by image analysis
Mass measured by weighting (g)
Golden_01_3.JPG
325456
713
620
223.61
Golden_02_3.JPG
292818
605
587
212.49
Golden_03_3.JPG
308248
691
573
216.70
Golden_04_3.JPG
330242
639
644
237.87
Golden_05_3.JPG
258536
606
551
170.47
Golden_06_3.JPG
324243
654
610
224.15
Golden_07_3.JPG
281514
616
581
197.17
Golden_08_3.JPG
292763
651
608
206.68
Golden_09_3.JPG
310381
659
624
215.81
Golden_10_3.JPG
262452
590
543
180.21
Golden_11_3.JPG
310246
652
594
228.82
Golden_12_3.JPG
295091
612
617
208.29
Golden_13_3.JPG
294691
687
599
208.82
Golden_14_3.JPG
318046
647
617
212.37
Golden_15_3.JPG
340940
693
633
236.41
Golden_16_3.JPG
317143
663
584
219.46
Golden_17_3.JPG
275108
590
587
189.83
Golden_18_3.JPG
328042
700
600
226.72
Golden_19_3.JPG
329161
672
623
236.05
Golden_20_3.JPG
333618
688
589
229.25
Golden_21_3.JPG
315626
645
609
219.08
Golden_22_3.JPG
279182
616
563
193.42
Golden_23_3.JPG
319838
644
607
227.74
Golden_24_3.JPG
336852
712
638
241.11
Golden_25_3.JPG
318909
621
636
230.26
Regression analysis for estimation of the weight
Parameters
Value
Coefficient
0.002
Intercept
215.755
Score
Value
Training
0.97
Test
0.96
Parameters
Value
Coefficient
0.0008
Intercept
16.0667
Score
Value
Training
0.91
Test
0.92
Cao Le Cong Minh - caolecongminh1997@gmail.com
