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Figure 4:
Performance Metrics Evaluation for the Proposed Architecture using Testing Datasets
| Sl.no | No of batches | No of Epochs | Precision (%) | Recall(%) | F1-Score |
|---|---|---|---|---|---|
| 01 | 160 | 40 | 96.4% | 96.1% | 96.3% |
| 02 | 160 | 80 | 96.43% | 96.31% | 96.3% |
| 03 | 160 | 120 | 97.6% | 97.41% | 97.52% |
| 04 | 160 | 160 | 97.53% | 97.12% | 97.32% |
| 05 | 160 | 200 | 96.4% | 96.3% | 96.2% |
| 06 | 160 | 240 | 96.3% | 96.2% | 96.1% |
| 07 | 160 | 280 | 96.2% | 96.1% | 96.1% |
Evaluation of Various Models for Predicting Crop Yield Productivity with Dropout Rate of 0_2
| Algorithm | Performance Metrics(%) | recall | Specificity | F1-score | |
|---|---|---|---|---|---|
| Accuracy | Precision | ||||
| LSTM | 82.5 | 83.5 | 83.4 | 84.1 | 84.2 |
| LSTM+PSO | 86.3 | 86.2 | 85.8 | 86.1 | 85.4 |
| LSTM+GA | 86.6 | 86.4 | 86.2 | 87.3 | 85.9 |
| LSTM+WOA | 88.4 | 87.3 | 86.8 | 87.1 | 86.9 |
| LSTM+SSO | 88.5 | 89.1 | 88.8 | 88.4 | 88.5 |
| LSTM+SHO | 91.6 | 89.7 | 89.4 | 89.4 | 88.4 |
Benchmarking assessment of the various methodologies in identifying the crop-yield productivity with the drop-out=0_6
| Algorithm | Performance Metrics(%) | recall | Specificity | F1-score | |
|---|---|---|---|---|---|
| Accuracy | Precision | ||||
| LSTM | 83.5 | 83.6 | 83.5 | 84.0 | 84 |
| LSTM+PSO | 88.3 | 86.3 | 85.9 | 86.0 | 85.4 |
| LSTM+GA | 88.3 | 86.5 | 86.3 | 87.4 | 85.9 |
| LSTM+WOA | 89.6 | 87.4 | 86.9 | 87.2 | 86.9 |
| LSTM+SSO | 89.9 | 89.0 | 88.9 | 88.5 | 88.5 |
| LSTM+SHO | 90.8 | 89.8 | 89.5 | 89.5 | 88.4 |
| PROPOSED MODEL | 97.6 | 96.9 | 96.7 | 96.6 | 96.5 |
Performance Metrics Evaluation for the Proposed Architecture using Verification/Validation Datasets
| Sl.no | No of batches | No of Epochs | Precision (%) | Recall(%) | F1-Score |
|---|---|---|---|---|---|
| 01 | 170 | 40 | 96.5% | 96.% | 96.3% |
| 02 | 170 | 80 | 96.45% | 96.3% | 96.3% |
| 03 | 170 | 120 | 97.5% | 96.9% | 97.51% |
| 04 | 170 | 160 | 97.3% | 97.1% | 97.31% |
| 05 | 170 | 200 | 96.45% | 96.30% | 96.4% |
| 06 | 170 | 240 | 96.30% | 96.20% | 96.20% |
| 07 | 170 | 280 | 96.20% | 96.10% | 96.17% |
Validation/Verification Accuracy Performance using the no of batches =160
| Sl.no | No of batches | No of Epochs | Validation /Verification Accuracy (%) |
|---|---|---|---|
| 01 | 160 | 40 | 96.34% |
| 02 | 160 | 80 | 97.6% |
| 03 | 160 | 120 | 98.55% |
| 04 | 160 | 160 | 98.35% |
| 05 | 160 | 200 | 98.21% |
| 06 | 160 | 240 | 98.3% |
| 07 | 160 | 280 | 98.2% |
Benchmarking assessment of the various methodologies in identifying the crop-yield productivity with the drop-out=0_4
| Algorithm | Performance Metrics(%) | recall | Specificity | F1-score | |
|---|---|---|---|---|---|
| Accuracy | Precision | ||||
| LSTM | 84.3 | 83.6 | 83.5 | 83.5 | 84 |
| LSTM+PSO | 87.6 | 86.3 | 85.9 | 88.3 | 85.4 |
| LSTM+GA | 87.5 | 86.5 | 86.3 | 88.3 | 85.9 |
| LSTM+WOA | 89.5 | 87.4 | 86.9 | 89.6 | 86.9 |
| LSTM+SSO | 89.4 | 89.0 | 88.9 | 89.9 | 88.5 |
| LSTM+SHA | 91.0 | 89.8 | 89.5 | 90.8 | 88.4 |
| PROPOSED MODEL | 97.3 | 96.9 | 96.7 | 96.9 | 96.5 |
Performance Metrices
| SL.NO | Performance Measures | Expression |
|---|---|---|
| 1 | Accuracy |
|
| 2 | Recall |
|
| 3 | Specificity |
|
| 4 | Precision |
|
| 5 | F1-Score |
|
Training Accuracy Performance using the no of batches =160
| Sl.no | No of batches | No of Epochs | Testing Accuracy (%) |
|---|---|---|---|
| 01 | 160 | 40 | 96.36% |
| 02 | 160 | 80 | 97.65% |
| 03 | 160 | 120 | 98.55% |
| 04 | 160 | 160 | 98.35% |
| 05 | 160 | 200 | 98.21% |
| 06 | 160 | 240 | 98.3% |
| 07 | 160 | 280 | 98.2% |