An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Materials and Equipment
2.2. Test Method
2.3. Building the ANN Model
2.3.1. Development of Neural Network Model
2.3.2. Model Inputs and Outputs
2.3.3. Data Division and Preprocessing
2.3.4. Model Architecture
2.3.5. Weight Optimization
2.3.6. Stopping Criteria and ANN Model Validation
3. Results
3.1. Evaluation of the Number of Hidden Layer Nodes
3.2. Evaluation of Prediction Results
3.3. Sensitivity Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Rice Varieties | Plant Height/mm | Panicle Length/mm | Middle Stem Diameter/mm | Middle Stem Wall Thickness/mm | Number of Shoots per Ear | Number of Grains per Ear | Thousand-Grain Mass/g | Stem Moisture Content/% | Grain Moisture Content /% | Yield per Unit Area /kg·hm−2 | Ratio of Grass to Grain |
---|---|---|---|---|---|---|---|---|---|---|---|
Xiangzaoxian No. 24 | 833 ± 64 | 182 ± 12 | 32.18 ± 0.3 | 0.4 ± 0.1 | 12 ± 1.6 | 110 ± 22.9 | 30.02 ± 1.0 | 55.68 ± 4.8 | 22.42 ± 0.8 | 6230 | 1:(0.83 ± 0.1) |
Parameters | Values |
---|---|
The total length of the cylinder/mm | 1935 |
Threshing cylinder diameter/mm | 620 |
Cylinder speed/(r·min−1) | 400–1500 |
Threshing clearance of concave sieve/mm | 0–60 |
Separating clearance of concave Sieve/mm | 0–60 |
Feeding rate/(kg·s−1) | 0.5–5 |
Parameters | Statistical Criteria | ||||||
---|---|---|---|---|---|---|---|
Minimum | Maximum | Average | Standard Deviation | Median | Variance | ||
Training set | inputs | 1 | 800 | 190.4500 | 300.8486 | 25 | 9.0154 × 104 |
outputs | 0.0490 | 1.1960 | 0.4142 | 0.4074 | 0.2070 | 0.1660 | |
Validation set | inputs | 2 | 800 | 183.3750 | 291.1664 | 30 | 8.4778 × 104 |
outputs | 0.0490 | 1.1960 | 0.3984 | 0.4231 | 0.1920 | 0.1790 | |
Testing set | inputs | 1.5 | 800 | 197.7750 | 311.6720 | 35 | 9.7139 × 104 |
outputs | 0.04093 | 0.9970 | 0.3995 | 0.4093 | 0.1920 | 0.1675 |
Factors | Rotational Speed of Cylinder, RS (r/min) | Threshing Clearance of Concave Sieve, TC (mm) | Separating Clearance of Concave Sieve, SC (mm) | Feeding Quantity, FQ (kg/s) | |
---|---|---|---|---|---|
Levels | |||||
1 | 600 | 15 | 15 | 1.0 | |
2 | 650 | 20 | 25 | 1.5 | |
3 | 700 | 25 | 35 | 2.0 | |
4 | 750 | 30 | 45 | 2.5 | |
5 | 800 | 35 | 55 | 3.0 |
Dataset | |||
---|---|---|---|
Training set | 0.97596 | 0.079148 | 0.14100 |
Validation set | 0.97981 | 0.13823 | 0.15260 |
Testing set | 0.99041 | 0.086466 | 0.13543 |
Sr.No. | Parameter | Description |
---|---|---|
1 | No. of input nodes | Varying from 1 to 25 in the cascaded training procedure |
2 | No. of output nodes | 3 |
3 | No. of hidden layers | 2 |
4 | No. of neurons in the hidden layer (Hn) | 5–3 |
5 | Training rule | Levenberg-Marquardt (LM) |
6 | Activation function | Sigmoid |
7 | Network type | Feed-forward (FF) |
8 | Training method | Backpropagation algorithm |
Trial No. | Relative Importance for Input Variables | |||
---|---|---|---|---|
RS | TC | SC | FQ | |
1 | 0.2417 | 0.1083 | 0.1208 | 0.1013 |
2 | 0.1831 | 0.1575 | 0.1468 | 0.086 |
3 | 0.1426 | 0.1278 | 0.1892 | 0.0828 |
4 | 0.0507 | 0.2191 | 0.1274 | 0.1805 |
Average | 0.1500 | 0.1489 | 0.1432 | 0.1165 |
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Ma, L.; Xie, F.; Liu, D.; Wang, X.; Zhang, Z. An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device. Agriculture 2023, 13, 788. https://doi.org/10.3390/agriculture13040788
Ma L, Xie F, Liu D, Wang X, Zhang Z. An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device. Agriculture. 2023; 13(4):788. https://doi.org/10.3390/agriculture13040788
Chicago/Turabian StyleMa, Lan, Fangping Xie, Dawei Liu, Xiushan Wang, and Zhanfeng Zhang. 2023. "An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device" Agriculture 13, no. 4: 788. https://doi.org/10.3390/agriculture13040788