Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network
Abstract
1. Introduction
2. Materials and Methods
2.1. Samples Used
2.2. Development of Visible and Near-Infrared Light Spectroscopy System
2.3. Extracting Color Features
2.4. Extraction of Chlorophyll b
2.5. Different Hybrids of Artificial Neural Networks Used for Selecting Effective Features and Predicting Chlorophyll b
2.6. Hybrid Neural Networks Used to Select the Most Effective Color Features
2.7. Neural Network Hybrid Used to Estimate the Amount of Chlorophyll b Using Color Features
2.8. Neural Network Hybrid Used to Select Effective Wavelengths
2.9. Neural Network Hybrid Used to Estimate Chlorophyll b Content Using Spectral Data
Algorithm 1: Algorithm of biogeography-based optimization. |
1: Production of initial populations and sorting 2: Determining rates of immigrant receptivity and receiving 3: For (habitat like j) 4: For (variable such as k in the habitat j) 5: With the probability of immigrant receptivity in a settlement in the variable, changes are applied according to steps 6 to 8 6: Determining the origin of immigration using immigrant receptivity values randomly 7: Immigration from a settlement to another 8: With a certain probability, are applied to the variable component (random changes (mutation)) 9: end for 10: end for 11: The set of new answers is evaluated 12: Combining the main population (old) and the population from migration Create a new stage population 13: If the termination conditions are not fulfilled, the algorithm will be returned to step 3 14: End process |
2.10. Parameters Used to Evaluate the Performance of Proposed Methods for Estimating the Amount of Chlorophyll b
3. Results and Discussion
3.1. Response of Apple Samples to Visible/Near-Infrared Wavelengths
3.2. Estimation of chlorophyll b Using Color Features
3.3. Estimation of Chlorophyll b Content Using Non-Destructive Spectroscopy Method
3.4. Analyzing the Performance of Chlorophyll b Predictive Systems Based on Color and Spectroscopy Methods
3.5. Effective Wavelengths Selected by the Hybrid Artificial Neural Network—Differential Evolution Algorithm
3.6. The Performance of the Chlorophyll b Estimation System Based on the Effective Wavelengths Selected
4. Conclusions
- The cost of the configuration and set-up of the spectroscopy system is very important for real time aims. To reduce the cost of configuration, a small window of around 680 nm wavelength could be used instead of using spectroscopy over the entire visible/near-infrared range.
- The largest peak in spectral diagrams in the visible light region is related to the chlorophyll absorption because the chlorophyll b content was predicted to be high when the coefficient was predicted using the relevant spectral data of this region.
- There is a relationship between the color features of the apple and the amount of chlorophyll b so that the chlorophyll b values are estimated using these color features, with a coefficient of more than 0.996.
- Performance of the spectral method is higher than the color method in terms of the determination and regression coefficients as well as the error estimation parameters.
- When effective spectra selected by the hybrid artificial neural network-differential evolution algorithm are introduced as an input to a hybrid artificial neural network-biogeography-based algorithm, it has high regression and determination coefficients.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Values |
---|---|
Number of layers | 2 |
Number of neurons | First layer: 11, Second layer: 15 |
Transfer function | First layer: tribas, Second layer: logsig |
Backpropagation network training function | trainscg |
Backpropagation weight/bias learning function | learnpn |
Parameter | Values |
---|---|
Number of layers | 2 |
Number of neurons | First layer: 19, Second layer: 13 |
Transfer function | First layer: radbas, Second layer: tansig |
Backpropagation network training function | traingda |
Backpropagation weight/bias learning function | learngd |
Parameter | Values |
---|---|
Number of layers | 3 |
Number of neurons | First layer: 9, Second layer: 19, Third layer: 7 |
Transfer function | First layer: satlin, Second layer: purelin, Third layer: satlins |
Backpropagation network training function | traingdm |
Backpropagation weight/bias learning function | learncon |
Statistics | MSE | RMSE | MAE | SSE | R |
---|---|---|---|---|---|
Mean ± Standard Deviation | 0.051 ± 0.054 | 0.209 ± 0.086 | 0.144 ± 0.055 | 0.665 ± 0.702 | 0.882 0.047 |
The best result | 0.002 | 0.039 | 0.031 | 0.021 | 0.9931 |
Parameter | Values |
---|---|
Number of layers | 3 |
Number of neurons | First layer: 16, Second layer: 17, Third layer: 13 |
Transfer function | First layer: softmax, Second layer: poslin, Third layer: tansig |
Backpropagation network training function | traingbr |
Backpropagation weight/bias learning function | learnlv2 |
Statistics | MSE | RMSE | MAE | SSE | R |
---|---|---|---|---|---|
Mean ± Standard Deviation | 0.025 ± 0.032 | 0.141 ± 0.073 | 0.099 ± 0.047 | 0.329 ± 0.421 | 0.932 0.046 |
The best result | 0.001 | 0.031 | 0.027 | 0.013 | 0.9965 |
Apple | Measured | Estimated | Apple | Measured | Estimated | Apple | Measured | Estimated |
---|---|---|---|---|---|---|---|---|
Number | Value | Value | Number | Value | Value | Number | Value | Value |
1 | 3.73 | 3.56 ± 0.169 | 15 | 3.71 | 3.54 ± 0.164 | 29 | 3.60 | 3.36 ± 0.140 |
2 | 3.15 | 3.13 ± 0.077 | 16 | 3.37 | 3.39 ± 0.128 | 30 | 3.14 | 3.12 ± 0.074 |
3 | 3.06 | 3.08 ± 0.062 | 17 | 3.75 | 3.59 ± 0.172 | 31 | 3.44 | 3.40 ± 0.125 |
4 | 3.29 | 3.31 ± 0.102 | 18 | 3.49 | 3.39 ± 0.117 | 32 | 3.08 | 3.09 ± 0.077 |
5 | 4.06 | 3.87 ± 0.218 | 19 | 4.86 | 3.51 ± 0.529 | 33 | 3.59 | 3.42 ± 0.134 |
6 | 3.61 | 3.43 ± 0.125 | 20 | 3.11 | 3.14 ± 0.074 | 34 | 3.00 | 2.96 ± 0.150 |
7 | 3.28 | 3.32 ± 0.108 | 21 | 3.02 | 2.99 ± 0.140 | 35 | 3.86 | 3.87 ± 0.263 |
8 | 3.04 | 3.07 ± 0.068 | 22 | 3.69 | 3.50 ± 0.157 | 36 | 3.19 | 3.23 ± 0.093 |
9 | 3.07 | 3.08 ± 0.057 | 23 | 3.13 | 3.12 ± 0.082 | 37 | 3.09 | 3.10 ± 0.159 |
10 | 3.09 | 3.09 ± 0.065 | 24 | 3.12 | 3.12 ± 0.092 | 38 | 3.04 | 3.02 ± 0.191 |
11 | 3.81 | 3.59 ± 0.173 | 25 | 3.42 | 3.43 ± 0.195 | 39 | 3.63 | 3.45 ± 0.136 |
12 | 3.82 | 3.75 ± 0.227 | 26 | 4.26 | 4.02 ± 0.241 | 40 | 3.23 | 3.27 ± 0.110 |
13 | 3.69 | 3.53 ± 0.159 | 27 | 3.04 | 3.05 ± 0.137 | 41 | 3.83 | 3.87 ± 0.292 |
14 | 4.67 | 3.08 ± 0.179 | 28 | 3.18 | 316 ± 0.213 | 42 | 3.06 | 3.07 ± 0.091 |
Apple | Measured | Estimated | Apple | Measured | Estimated | Apple | Measured | Estimated |
---|---|---|---|---|---|---|---|---|
Number | Value | Value | Number | Value | Value | Number | Value | Value |
1 | 3.73 | 3.59 ± 0.167 | 15 | 3.71 | 3.56 ± 0.188 | 29 | 3.60 | 3.40 ± 0.119 |
2 | 3.15 | 3.21 ± 0.079 | 16 | 3.37 | 3.28 ± 0.090 | 30 | 3.14 | 3.16 ± 0.069 |
3 | 3.06 | 3.08 ± 0.049 | 17 | 3.75 | 3.62 ± 0.181 | 31 | 3.44 | 3.34 ± 0.107 |
4 | 3.29 | 3.28 ± 0.089 | 18 | 3.49 | 3.33 ± 0.105 | 32 | 3.08 | 3.09 ± 0.050 |
5 | 4.06 | 3.95 ± 0.249 | 19 | 3.84 | 4.55 ± 0.328 | 33 | 3.59 | 3.36 ± 0.121 |
6 | 3.61 | 3.55 ± 0.173 | 20 | 3.11 | 3.10 ± 0.056 | 34 | 3.00 | 2.96 ± 0.147 |
7 | 3.28 | 3.24 ± 0.083 | 21 | 3.02 | 3.04 ± 0.085 | 35 | 3.86 | 3.65 ± 0.241 |
8 | 3.04 | 3.07 ± 0.066 | 22 | 3.69 | 3.56 ± 0.164 | 36 | 3.19 | 3.22 ± 0.081 |
9 | 3.07 | 3.08 ± 0.069 | 23 | 3.13 | 3.18 ± 0.228 | 37 | 3.09 | 3.08 ± 0.058 |
10 | 3.09 | 3.09 ± 0.052 | 24 | 3.12 | 3.12 ± 0.064 | 38 | 3.04 | 3.05 ± 0.091 |
11 | 3.81 | 3.61 ± 0.176 | 25 | 3.42 | 3.33 ± 0.098 | 39 | 3.63 | 3.54 ± 0.159 |
12 | 3.82 | 3.66 ± 0.211 | 26 | 4.26 | 4.06 ± 0.228 | 40 | 3.23 | 3.28 ± 0.086 |
13 | 3.69 | 3.51 ± 0.154 | 27 | 3.04 | 3.04 ± 0.089 | 41 | 3.83 | 3.74 ± 0.239 |
14 | 4.67 | 4.03 ± 0.177 | 28 | 3.18 | 319 ± 0.069 | 42 | 3.06 | 3.07 ± 0.079 |
Number of Effective Wavelengths | Effective Wavelengths |
---|---|
2 | 687.152, 662.295 |
4 | 664.006, 687.724, 673.425, 697.180 |
6 | 669.428, 664.862, 680.571, 696.033, 683.431, 677.711 |
8 | 683.431, 997.753, 666.287, 685.148, 674.568, 671.141, 672.283, 684.862 |
10 | 662.866, 686.007, 671.997, 676.282, 696.033, 689.156, 690.015, 686.293, 693.453, 686.865 |
The Number of Selected Effective Waveleghs | Statistics | MSE | RMSE | MAE | SSE | R2 |
---|---|---|---|---|---|---|
Mean ± | ||||||
2 | Standard Deviation | 0.032 ± 0.044 | 0.155 ± 0.095 | 0.099 ± 0.054 | 0.414 ± 0.5771 | 0.915 ± 0.106 |
The best result | 0.006 | 0.025 | 0.019 | 0.008 | 0.997 | |
Mean ± | ||||||
4 | Standard Deviation | 0.028 ± 0.112 | 0.138 ± 0.097 | 0.095 ± 0.073 | 0.368 ± 0.455 | 0.926 ± 0.087 |
The best result | 0.0007 | 0.027 | 0.022 | 0.009 | 0.998 | |
Mean ± | ||||||
6 | Standard Deviation | 0.026 ± 0.045 | 0.135 ± 0.087 | 0.093 ± 0.051 | 0.339 ± 0.587 | 0.924 ± 0.094 |
The best result | 0.0007 | 0.026 | 0.020 | 0.008 | 0.996 | |
Mean ± | ||||||
8 | Standard Deviation | 0.024 ± 0.034 | 0.134 ± 0.078 | 0.094 ± 0.048 | 0.315 ± 0.448 | 0.925 ± 0.090 |
The best result | 0.0010 | 0.033 | 0.022 | 0.014 | 0.997 | |
Mean ± | ||||||
10 | Standard Deviation | 0.024 ± 0.038 | 0.133 ± 0.079 | 0.093 ± 0.049 | 0.313 ± 0.496 | 0.930 ± 0.08 |
The best result | 0.001 | 0.031 | 0.027 | 0.013 | 0.9965 |
Method | Type of Fruit | Coefficient of Determination |
---|---|---|
Propose method using spectral features | Apple | 0.998 |
Propose method using color features | Apple | 0.996 |
Ncama et al. [20] | Grapefruit | 0.943 |
Adebayo et al. [44] | Banana | 0.978 |
Betemps et al. [45] | Apple | 0.934 |
Kuckenberg et al. [46] | Apple | 0.927 |
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Abbaspour-Gilandeh, Y.; Sabzi, S.; Hernández-Hernández, M.; Hernández-Hernández, J.L.; Azadshahraki, F. Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network. Agronomy 2019, 9, 735. https://doi.org/10.3390/agronomy9110735
Abbaspour-Gilandeh Y, Sabzi S, Hernández-Hernández M, Hernández-Hernández JL, Azadshahraki F. Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network. Agronomy. 2019; 9(11):735. https://doi.org/10.3390/agronomy9110735
Chicago/Turabian StyleAbbaspour-Gilandeh, Yousef, Sajad Sabzi, Mario Hernández-Hernández, Jose Luis Hernández-Hernández, and Farzad Azadshahraki. 2019. "Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network" Agronomy 9, no. 11: 735. https://doi.org/10.3390/agronomy9110735
APA StyleAbbaspour-Gilandeh, Y., Sabzi, S., Hernández-Hernández, M., Hernández-Hernández, J. L., & Azadshahraki, F. (2019). Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network. Agronomy, 9(11), 735. https://doi.org/10.3390/agronomy9110735