Table 1.
Chemical properties of perlite.
| Ingredients | % | Ingredients | % |
|---|---|---|---|
| SiO2 | 71.0–75.0 | Cr | 0.0–0.1 |
| AlO3 | 12.5–18.0 | Ba | 0.0–0.05 |
| Na2O3 | 2.9–4.0 | PbO | 0.0–0.03/0.3 |
| K2O | 0.5–5.0 | NiO | Trace amount |
| CaO | 0.5–0.2 | Cu | Trace amount |
| Fe2O3 | 0.1–1.5 | B | Trace amount |
| MgO | 0.02–0.5 | Be | Trace amount |
| TiO2 | 0.03–0.2 | free silica | 0.0–0.2 |
| MnO2 | 0.0–0.1 | Total chlorides | Trace amount – 0.2 |
| SO3 | 0.0–0.2 | Total sulphates | None |
| FeO | 0.0–0.1 |
Figure 1.
The MLP structure with seven inputs, five outputs, and five hidden neurons. MLP, multilayer perceptron.
Table 2.
Formulas used in evaluation.
| Formula | Definition |
|---|---|
| 1 | The TPR is the proportion of positive instances that are correctly classified by the model. Where TP is the number of true positive instances, and FN is the number of false negative instances. The larger the value the better |
| 2 | FPR indicates the probability that a positive decision is wrong. The smaller the value, the better the performance of the model |
| 3 | Precision is the ratio of the samples correctly predicted by the model to all the samples positively predicted |
| 4 | The F-measure is defined as the weighted harmonic mean of precision and recall |
| 5 | The accuracy of correct classification ranges between 0.5 and 1, where higher values indicate a better classifier. Accuracy values between 0.7 and 1 are generally considered acceptable |
Figure 2.
Change rates of plant upper part characteristics compared to the control values of the analysis results after vermicompost applications.
Table 3.
Effect of vermicompost application on plant upper part characteristics.
| Applications | Plant height (cm) | Number of shoots | Shoot length (mm) | Leaf width (cm) | Leaf length (cm) |
|---|---|---|---|---|---|
| Control | 11.5 ab | 7.7 c | 5.6 c | 7.7 c | 23.4 c |
| 10 mL | 10.9 b | 11.2 a | 6.0 ab | 8.4 b | 26.4 ab |
| 20 mL | 10.2 c | 8.7 b | 5.6 c | 9.6 a | 25.8 b |
| 40 mL | 11.4 ab | 8.5 b | 7.0 a | 9.0 ab | 27.1 a |
| 80 mL | 11.8 a | 7.4 c | 5.8 bc | 8.6 b | 25.8 b |
Figure 3.
Effect of vermicompost applications on leaf chlorophyll content.
Figure 4.
The rate of change of root architectural features compared to the control values of the analysis results after vermicompost applications.
Table 4.
Effect of vermicompost application on root architectural properties.
| Applications | Root length (cm) | Root surface area (cm2) | Root volume (cm3) | Root diameter (mm) | Number of tips | Number of forks | Number of crossings |
|---|---|---|---|---|---|---|---|
| Control | 35.60 ab | 2.90 a | 22 bc | 3 a | 17.99 ab | 6.77 a | 590 a |
| 10 mL | 37.34 a | 2.57 b | 15 c | 2 b | 18.84 a | 6.37 ab | 572 ab |
| 20 mL | 29.28 c | 2.85 a | 26 b | 3 a | 14.38 b | 5.19 c | 440 b |
| 40 mL | 31.20 bc | 2.65 b | 31 a | 3 a | 14.62 b | 5.09 c | 449 b |
| 80 mL | 33.46 b | 2.75 ab | 23 bc | 3 a | 14.10 b | 5.76 b | 470 b |
Figure 5.
The rate of change of nutrient content in roots.
Table 5.
Effect of vermicompost applications on root nutrient content.
| Application | N | P | K | Ca | Mg | Fe | Cu | Zn | Mn |
|---|---|---|---|---|---|---|---|---|---|
| % | mg · kg−1 | ||||||||
| Control | 2.99 b | 2075 a | 18991 c | 6309 a | 2876 a | 181 ab | 10.4 b | 58.4 a | 146 b |
| 10 mL | 3.93 a | 1605 b | 20914 b | 6052 b | 2369 b | 160 b | 7.7 c | 31.3 c | 229 a |
| 20 mL | 3.02 b | 1594 b | 24264 ab | 6386 a | 2744 ab | 224 a | 18.4 a | 41.8 bc | 118 c |
| 40 mL | 3.73 ab | 1473 bc | 29988 a | 6200 ab | 1919 bc | 140 c | 8.6 c | 44.1 b | 123 bc |
| 80 mL | 2.87 c | 1230 c | 25720 ab | 5068 c | 1739 c | 143 c | 15.0 ab | 43.5 b | 75.0 d |
Figure 6.
DT obtained by J.48 method. DT, decision tree.
Figure 7.
Comparison of performance levels of models created using PART, J48, Multilayer Perceptron, and Multi Clas Classifier algorithms.
Figure 8.
The predictive power of ML models. ML, machine learning.