A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron
Abstract
:1. Introduction
- Acquiring the crop-related data for training the model and analyzing the factors influencing crop production.
- Performing Feature Engineering for localizing the features contributing to precise crop yield analysis.
- Training the model and analyzing the hyperparameters to read the data’s insights adequately.
- The Spider Monkey Optimization technique optimizes the Multi-Layer Perceptron model for analyzing the outcome.
- Analyzing the model’s performance with various evolution metrics such as sensitivity, specificity, F1- Score, and accuracy measures.
- The prediction efficiency of the model is being analyzed against the other state of art techniques used in crop yield prediction.
2. Literature Review
3. Background
3.1. Data Normalization
3.2. Feature Engineering
4. Proposed Method
4.1. Multi-Layer Perceptron Model
4.2. Neuron Selection Using Spider Monkey Optimization
4.3. Dataset Collection
4.4. Details of Implementation Platform
5. Results and Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Year | Crop | Technique | Outcome |
---|---|---|---|---|
Krithika K.M. et al. [35] | 2022 | Groundnut |
|
|
Amna Ikram et al. [36] | 2022 | General Analysis |
|
|
Kumar Raj and Singhal Vivek [37] | 2022 | General Analysis |
|
|
Vinson Joshua et al. [38] | 2022 | General Analysis |
|
|
Vignesh et al. [39] | 2022 | General Analysis |
|
|
Paudel et al. [40] | 2021 | Soft wheat Spring barley Sunflower Sugar beet Potatoes |
|
|
Bali et al. [41] | 2021 | Wheat |
|
|
Rajagopal [42] | 2021 | General Analysis |
|
|
Implementation Environment | Details |
---|---|
Processor | Intel Core i7-1260P (12 Gen) |
Make | HP Pavilion 15-EG2039TU |
Architecture | 64 |
Operating System | Windows 11 |
Memory Allotted | 3 GB |
GPU | Iris Xe |
Coding language | Python |
Framework | Jupiter Notebook v6.5.1 |
Libraries used | sklearn, PyTorch, NumPy, pandas |
Approach | RMSE (Mg/Ha) | R2 | MBE (Mg/Ha) |
---|---|---|---|
LASSO [34] | 1.11 | 0.67 | −0.48 |
LightGBM [34] | 1.0 | 0.75 | −0.06 |
XGBoost [34] | 0.99 | 0.75 | −0.13 |
RF [34] | 1.12 | 0.68 | −0.14 |
LR [34] | 1.12 | 0.68 | 0.03 |
OWE [34] | 0.99 | 0.75 | −0.06 |
LSTM Model with Adam [54] | 0.02 | 0.96 | |
MLP alone | 0.13 | 0.96 | −0.10 |
MLP with SMO | 0.11 | 0.98 | −0.19 |
Approach | RMSE (Mg/Ha) | R2 | MAE |
---|---|---|---|
BPNM [38] | 0.29 | 0.89 | 0.21 |
SVM [38] | 0.23 | 0.93 | |
GRNN [38] | 0.16 | 0.97 | 0.08 |
SVR [55] | 0.06 | 0.83 | 0.17 |
GPR [55] | 0.05 | 0.90 | 0.13 |
ANN [55] | 0.17 | 0.92 | 0.17 |
RF [55] | 0.17 | 0.89 | 0.14 |
DT [56] | 0.54 | 0.42 | 1.21 |
LR [56] | 0.49 | 0.53 | 1.41 |
RF [56] | 0.36 | 0.75 | 0.41 |
ANN [56] | 0.37 | 0.62 | 0.22 |
Gradient Boosting [57] | 0.53 | 0.54 | 0.41 |
DRL [57] | 0.17 | 0.87 | 0.13 |
MLP with SMO | 0.11 | 0.98 | 0.09 |
Approach | Training RMSE | Validation RMSE | Training Correlation | Validation Correlation |
---|---|---|---|---|
MLP | 0.12 | 0.14 | 93.9% | 82.2% |
MLP with SMO | 0.09 | 0.11 | 95.4% | 86.9% |
MLP | MLP with SMO | |
---|---|---|
Wins (+) | 2000 | 2061 |
Loses (−) | 139 | 78 |
MLP | MLP with SMO | |
---|---|---|
Average error | 0.197 | 0.183 |
Average Fitness | 0.271 | 0.259 |
Best Fitness | 0.187 | 0.165 |
Worst Fitness | 0.292 | 0.274 |
R+ | 51 | 51 |
R− | 0 | 0 |
Significant(alpha) | 0.05 | 0.05 |
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Ahmed, S. A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron. Sustainability 2023, 15, 3017. https://doi.org/10.3390/su15043017
Ahmed S. A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron. Sustainability. 2023; 15(4):3017. https://doi.org/10.3390/su15043017
Chicago/Turabian StyleAhmed, Shakeel. 2023. "A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron" Sustainability 15, no. 4: 3017. https://doi.org/10.3390/su15043017
APA StyleAhmed, S. (2023). A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron. Sustainability, 15(4), 3017. https://doi.org/10.3390/su15043017