Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Methods
2.2. Measurement of Characteristic Parameters of the Corn Ear
2.2.1. Measurement of the Area of the Top of All the Corn Kernels
2.2.2. Measurement of Corncob Parameters
2.3. The Method of Establishing the Regression Model
2.4. Acquisition and Processing of Corn Images
2.5. Automatic Measurement Method of Corncob Parameters
3. Results and Discussion
3.1. The Result of Establishing the Regression Model
3.2. The Results of Image Processing and Corncob Parameters’ Automatic Measurement
3.3. The Results of Detection of the Threshing Rate of Corn Ears
4. Conclusions
- (1)
- The regression model for restoring the area of the top of all corn kernels of an ear by midsection radius and length of the corncob was established. The regression equation with the R2 value exceeding 0.98 demonstrates a strong level of fit.
- (2)
- A method of measuring corncobs’ parameters by image processing is proposed. The maximum relative error of length and midsection radius was 7.46% and 5.55%, and the mean relative error was 2.58% and 2.23%.
- (3)
- A method based on machine vision to detect the threshing rate of corn ears by the area of the top of corn kernels was proposed. Compared with the weighing method, the maximum relative error of automatic measurement is 7.08%, and the mean relative error is 2.04%. When the residual kernels were concentrated in the midsection, the inspection result of the corn ear threshing rate was better. The maximum relative error was 3.98%, and the mean relative error was 1.07%.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Independent Variable | Dependent Variable | R2 | p |
---|---|---|---|
Small-section radius, Length | Area of the top of all corn kernels | 0.948 | <0.001 |
Midsection radius, Length | 0.987 | <0.001 | |
Large-section radius, Length | 0.960 | <0.001 |
Statistical Magnitude | Length (cm) | Midsection Radius (cm) |
---|---|---|
Max. | 19.854 | 1.576 |
Min. | 13.091 | 1.314 |
Mean | 16.539 | 1.4666 |
S.D. | 1.4002 | 0.0527 |
p | 0.515 | 0.235 |
Unstandardized Coefficients | Beta | t | p | VIF | ||
---|---|---|---|---|---|---|
B | Std. Error | |||||
Constant | −220.976 | 12.602 | 17.535 | <0.0001 | ||
Length | 14.567 | 0.310 | 0.845 | 47.061 | <0.0001 | 1.164 |
Midsection radius | 150.946 | 9.195 | 0.295 | 16.416 | <0.0001 | 1.164 |
R | R2 | Adjusted R2 | D-W |
---|---|---|---|
0.993a | 0.987 | 0.986 | 2.053 |
Sum of Squares | df | Mean Square | F | p | |
---|---|---|---|---|---|
Regression | 24,803.425 | 2 | 12,401.712 | 1783.147 | <0.001 |
Residuals | 326.883 | 47 | 6.955 | ||
Total | 25,130.308 | 49 |
Statistical Magnitude | Length (cm) | Midsection Radius (cm) |
---|---|---|
Mean | 16.136 | 1.307 |
Max. relative error | 7.46% | 5.55% |
Min. relative error | 0.05% | 0.03% |
Mean relative error | 2.58% | 2.32% |
Corncob | The Weighing Method | Image Processing | Error | ||||||
---|---|---|---|---|---|---|---|---|---|
Large | Mid | Small | Large | Mid | Small | Large | Mid | Small | |
1 | 92.94% | 97.39% | 97.39% | 95.36% | 97.37% | 96.39% | 2.42% | 0.02% | 0.10% |
2 | 93.88% | 95.27% | 95.27% | 96.37% | 94.96% | 96.23% | 2.49% | 0.31% | 1.59% |
3 | 93.01% | 97.02% | 97.02% | 94.78% | 95.90% | 95.26% | 1.77% | 1.12% | 1.34% |
4 | 98.27% | 89.23% | 89.23% | 94.43% | 92.47% | 96.13% | 3.84% | 3.24% | 4.67% |
5 | 89.40% | 87.00% | 87.00% | 91.73% | 90.61% | 93.65% | 2.33% | 3.61% | 5.84% |
6 | 91.77% | 92.47% | 92.47% | 93.33% | 93.00% | 94.77% | 1.56% | 0.53% | 0.17% |
7 | 79.65% | 96.51% | 96.51% | 85.72% | 97.50% | 97.02% | 6.07% | 0.99% | 1.82% |
8 | 92.75% | 91.37% | 91.37% | 95.84% | 92.60% | 98.00% | 3.09% | 1.23% | 2.79% |
9 | 82.43% | 88.05% | 88.05% | 80.12% | 88.46% | 90.88% | 2.31% | 0.41% | 1.85% |
10 | 83.18% | 90.32% | 90.32% | 79.27% | 90.86% | 90.85% | 3.91% | 0.54% | 3.27% |
11 | 90.16% | 92.17% | 92.17% | 87.47% | 93.13% | 97.45% | 2.69% | 0.96% | 3.83% |
12 | 90.93% | 88.14% | 88.14% | 88.62% | 88.50% | 97.36% | 2.31% | 0.36% | 2.78% |
13 | 95.58% | 97.05% | 97.05% | 97.56% | 98.00% | 93.97% | 1.98% | 0.95% | 1.83% |
14 | 95.25% | 91.39% | 91.39% | 97.71% | 92.22% | 96.65% | 2.46% | 0.83% | 2.09% |
15 | 95.44% | 96.23% | 96.23% | 97.91% | 97.28% | 94.51% | 2.47% | 1.05% | 2.13% |
16 | 94.75% | 92.48% | 92.48% | 97.99% | 92.21% | 89.09% | 3.24% | 0.27% | 2.12% |
17 | 89.92% | 92.97% | 92.97% | 92.32% | 93.88% | 85.82% | 2.40% | 0.91% | 2.55% |
18 | 94.64% | 88.48% | 88.48% | 97.82% | 87.94% | 85.88% | 3.18% | 0.54% | 4.41% |
19 | 89.97% | 91.16% | 91.16% | 88.26% | 92.16% | 98.92% | 1.71% | 1.00% | 1.48% |
20 | 90.66% | 91.78% | 91.78% | 89.08% | 92.81% | 92.76% | 1.58% | 1.03% | 1.94% |
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Li, X.; Xu, S.; Zhang, W.; Wang, J.; Li, Y.; Peng, B.; Sun, R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture 2024, 14, 1037. https://doi.org/10.3390/agriculture14071037
Li X, Xu S, Zhang W, Wang J, Li Y, Peng B, Sun R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture. 2024; 14(7):1037. https://doi.org/10.3390/agriculture14071037
Chicago/Turabian StyleLi, Xinping, Shendi Xu, Wantong Zhang, Junyi Wang, Yanan Li, Bin Peng, and Ruizhe Sun. 2024. "Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision" Agriculture 14, no. 7: 1037. https://doi.org/10.3390/agriculture14071037