Author Contributions
Conceptualization, M.Z. and K.W.; methodology, M.Z.; software, K.W.; validation, J.W. and G.W.; formal analysis, K.W.; investigation, M.Z.; resources, M.Z.; data curation, K.W.; writing—original draft preparation, K.W.; writing-review and editing, M.Z.; visualization, K.W.; supervision, X.C. project administration, M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.
Figure 1.
The structure of the monitoring system for harvested corn grains with impurities and breakage rates.
Figure 1.
The structure of the monitoring system for harvested corn grains with impurities and breakage rates.
Figure 2.
Discrete tile sampling mechanism for corn harvested kernels. (1) Industrial computer, (2) industrial camera, (3) LED light source, (4) adjustable baffle, (5) gear shaft, (6) timing belt, (7) discharge tongue, (8) collection box, (9) outer sheaf, (10) corn kernels, (11) reducer, (12) DC motor, (13) stepper motor, and (14) camera fixing shell.
Figure 2.
Discrete tile sampling mechanism for corn harvested kernels. (1) Industrial computer, (2) industrial camera, (3) LED light source, (4) adjustable baffle, (5) gear shaft, (6) timing belt, (7) discharge tongue, (8) collection box, (9) outer sheaf, (10) corn kernels, (11) reducer, (12) DC motor, (13) stepper motor, and (14) camera fixing shell.
Figure 3.
Workflow of the monitoring system.
Figure 3.
Workflow of the monitoring system.
Figure 4.
Grain image and grayscale histogram under different brightness conditions.
Figure 4.
Grain image and grayscale histogram under different brightness conditions.
Figure 5.
Corn kernel harvest image preprocessing. (a) Original material collection image. (b) Image after filtering and open arithmetical processing. (c) Binary image. (d) Preprocessed images.
Figure 5.
Corn kernel harvest image preprocessing. (a) Original material collection image. (b) Image after filtering and open arithmetical processing. (c) Binary image. (d) Preprocessed images.
Figure 6.
Basic steps of genetic algorithm optimization.
Figure 6.
Basic steps of genetic algorithm optimization.
Figure 7.
Linear regression analysis of pixel area and mass. (a) Relationship between the quality of the whole corn kernel and the pixel area, (b) relationship between broken corn kernel quality and pixel area (c) relationship between impurity quality and pixel area, (d) residual analysis of intact corn mass and pixel area, (e) residual analysis of broken corn quality and pixel area, and (f) residual analysis of impurity mass and pixel area.
Figure 7.
Linear regression analysis of pixel area and mass. (a) Relationship between the quality of the whole corn kernel and the pixel area, (b) relationship between broken corn kernel quality and pixel area (c) relationship between impurity quality and pixel area, (d) residual analysis of intact corn mass and pixel area, (e) residual analysis of broken corn quality and pixel area, and (f) residual analysis of impurity mass and pixel area.
Figure 8.
Diagram of mass-prediction calculation steps.
Figure 8.
Diagram of mass-prediction calculation steps.
Figure 9.
Image acquisition and recognition experiment of harvested corn grains. (1) Grain churn, (2) grain tank, (3) corn harvesting grain sampling monitoring device, (4) industrial camera, and (5) motor.
Figure 9.
Image acquisition and recognition experiment of harvested corn grains. (1) Grain churn, (2) grain tank, (3) corn harvesting grain sampling monitoring device, (4) industrial camera, and (5) motor.
Figure 10.
Online monitoring software interface.
Figure 10.
Online monitoring software interface.
Figure 11.
Image processing and labeling of harvested corn grains. (a) Grayscale image of harvested corn grains. (b) Boundary suppression binary map. (c) Corn grain harvest image marker.
Figure 11.
Image processing and labeling of harvested corn grains. (a) Grayscale image of harvested corn grains. (b) Boundary suppression binary map. (c) Corn grain harvest image marker.
Figure 12.
Histograms of mass test results of various substances such as corn grains. (a) Manual test data results. (b) Machine inspection data results.
Figure 12.
Histograms of mass test results of various substances such as corn grains. (a) Manual test data results. (b) Machine inspection data results.
Table 1.
Factor level table of orthogonal tests.
Table 1.
Factor level table of orthogonal tests.
Level | Sheaf Speed A (r/min) | Conveyor Belt Speed B (r/min) | Adjust the Baffle Clearance C/mm |
---|
1 | 10 | 69.2 | 10 |
2 | 7.5 | 58.1 | 8 |
3 | 5 | 46 | 6 |
Table 2.
Test scheme and results.
Table 2.
Test scheme and results.
Serial Number | Sheaf Speed A | Conveyor Belt Speed B | Adjust the Baffle Clearance | Grain Recognition Accuracy Z1 | Grain Conveying Flow Z2/(g/s) |
---|
1 | 1 | 2 | 2 | 88.69% | 9.90 |
2 | 1 | 1 | 1 | 92.93% | 9.37 |
3 | 2 | 1 | 3 | 92.52% | 8.20 |
4 | 2 | 3 | 2 | 93.09% | 7.57 |
5 | 3 | 3 | 1 | 92.62% | 6.27 |
6 | 3 | 2 | 3 | 93.43% | 5.41 |
7 | 3 | 1 | 2 | 92.00% | 3.67 |
8 | 1 | 3 | 3 | 85.47% | 9.22 |
9 | 2 | 2 | 1 | 90.33% | 6.38 |
Table 3.
Range analysis.
Index | | Sheaf Speed A | Conveyor Belt Speed B | Adjust Baffle Clearance |
---|
Recognition accuracy Z1 | 1 | 89.03% | 92.48% | 91.96% |
2 | 91.98% | 90.82% | 91.26% |
3 | 92.68% | 90.39% | 90.47% |
| Range R | 3.65% | 2.09% | 1.49% |
| Factor, primary and secondary | A, B, C |
Grain conveying flow Z2 (g/s) | 1 | 9.49 | 7.08 | 7.34 |
2 | 7.38 | 7.23 | 7.05 |
3 | 5.12 | 7.69 | 7.61 |
| Range R | 4.37 | 0.61 | 0.56 |
| Factor, primary and secondary | A, B, C |
Table 4.
Distribution probabilities of luminance values for different threshold boundaries of images with different luminances.
Table 4.
Distribution probabilities of luminance values for different threshold boundaries of images with different luminances.
Threshold Interval | Grayscale Mean 94/% | Grayscale Mean 154/% | Gray Mean 117/% | Grayscale Mean 56/% | Gray Mean 18/% |
---|
[0, 0.4] | 58.93 | 43.31 | 54.45 | 77.80 | 100 |
[0, 0.35] | 56.55 | 36.47 | 53.02 | 71.31 | 100 |
[0, 0.3] | 54.33 | 28.26 | 51.68 | 65.61 | 99.99 |
[0, 0.25] | 52.46 | 28.89 | 49.70 | 60.52 | 99.84 |
Table 5.
Statistics of variation range of corn kernel and impurity characteristics and threshold setting.
Table 5.
Statistics of variation range of corn kernel and impurity characteristics and threshold setting.
The Characteristic Type | Complete Grains | Crush the Grain | Impurity | Threshold Range | Operation Logic |
---|
First-order invariant moment | 0.161~0.215 | 0.160~0.273 | 0.175~0.703 | >0.273 | or |
R-B value | 0.155~0.496 | 0.007~0.253 | −0.069~0.054 | <0.07 | or |
Border pixels | 132~329 | 111~404 | 117~458 | >404 | or |
Length-to-diameter ratio | 1.121~3.023 | 1.046~3.423 | 1.154~8.203 | >3.423 | or |
Table 6.
Image recognition results of harvested corn kernels.
Table 6.
Image recognition results of harvested corn kernels.
Number | Total Number of Grains | Identify the Exact Number | The Number of Misidentifications | Identification Accuracy/% |
---|
1 | 120 | 111 | 9 | 92.5 |
2 | 129 | 123 | 6 | 95.35 |
3 | 105 | 97 | 8 | 92.38 |
Table 7.
Test results of impurity content and breakage rate.
Table 7.
Test results of impurity content and breakage rate.
Group Number | Image Detection | Manual Inspection | Crushing Rate Absolute Error/% | Contains Impurity Rate Absolute Error/% | Single Picture Processing Time/s |
---|
Crushing Rate/% | Impurity Rate/% | Crushing Rate/% | Impurity Rate/% |
---|
1 | 11.48 | 0.79 | 10.64 | 1.16 | 0.84 | 0.37 | 1.67 |
2 | 13.09 | 1.74 | 10.44 | 1.14 | 2.65 | 0.6 | 1.76 |
3 | 15.69 | 2.21 | 13.53 | 1.18 | 2.16 | 1.03 | 1.71 |
average value | \ | \ | \ | \ | 1.88 | 0.67 | 1.71 |
Table 8.
The results of the first experiment were dynamically monitored for the yield of harvested maize grains’ impurity and crushing rate.
Table 8.
The results of the first experiment were dynamically monitored for the yield of harvested maize grains’ impurity and crushing rate.
Number | Sample Group | Image Detection | Manual Inspection | Absolute Error of Crushing Rate/% | Contains Impurity Absolute Error/% |
---|
Crushing Rate/% | Impurity Rate/% | Crushing Rate/% | Impurity Rate/% |
---|
1 | 1 | 7.58 | 1.46 | 8.63 | 0.41 | 1.05 | 1.05 |
2 | 1 | 6.82 | 2.02 | 8.63 | 0.41 | 1.81 | 1.61 |
3 | 1 | 7.65 | 1.34 | 8.63 | 0.41 | 0.98 | 0.93 |
4 | 2 | 6.79 | 1.42 | 8.97 | 0.56 | 2.18 | 0.86 |
5 | 2 | 6.58 | 1.63 | 8.97 | 0.56 | 2.39 | 1.07 |
6 | 2 | 5.95 | 2.34 | 8.97 | 0.56 | 3.02 | 1.78 |
7 | 3 | 7.44 | 2.00 | 8.82 | 1.16 | 1.38 | 0.84 |
8 | 3 | 5.87 | 2.24 | 8.82 | 1.16 | 2.95 | 1.08 |
9 | 3 | 8.06 | 1.71 | 8.82 | 1.16 | 0.76 | 0.55 |
| average value | \ | \ | \ | \ | 1.84 | 1.09 |
Table 9.
The results of the second experiment were dynamically monitored by the yield of harvested maize grains’ impurity and crushing rate.
Table 9.
The results of the second experiment were dynamically monitored by the yield of harvested maize grains’ impurity and crushing rate.
Number | Sample Group | Image Detection | Manual Inspection | Absolute Error of Crushing Rate/% | Contains Impurity Absolute Error/% |
---|
Crushing Rate/% | Impurity Rate/% | Crushing rate/% | Impurity Rate/% |
---|
1 | 1 | 19.55 | 1.05 | 22.83 | 1.02 | 3.28 | 0.03 |
2 | 1 | 21.12 | 0 | 22.83 | 1.02 | 1.71 | 1.02 |
3 | 1 | 20.22 | 2.06 | 22.83 | 1.02 | 2.61 | 1.04 |
4 | 2 | 21.15 | 1.29 | 21.92 | 1.13 | 0.77 | 0.16 |
5 | 2 | 20.12 | 0 | 21.92 | 1.13 | 1.8 | 1.13 |
6 | 2 | 19.7 | 3.13 | 21.92 | 1.13 | 2.22 | 2 |
7 | 3 | 22.73 | 0 | 23.24 | 1.34 | 0.51 | 1.34 |
8 | 3 | 19.76 | 1.05 | 23.24 | 1.34 | 3.48 | 0.29 |
9 | 3 | 20.5 | 0.6 | 23.24 | 1.34 | 2.74 | 0.74 |
| average value | \ | \ | \ | \ | 2.12 | 0.86 |