Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation

Parijata Majumdar

doi:10.62762/TSEL.2025.947593

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Volume 1, Issue 1, ICCK Transactions on Swarm and Evolutionary Learning

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ICCK Transactions on Swarm and Evolutionary Learning, Volume 1, Issue 1, 2025: 3-11

Free to Read | Research Article | 29 May 2025

Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation

Parijata Majumdar 1 *

1 Indian Institute of Information Technology Agartala, Barjala, Tripura 799046, India

* Corresponding Author: Parijata Majumdar, [email protected]

DOI: 10.62762/TSEL.2025.947593

Received: 23 April 2025, Accepted: 04 May 2025, Published: 29 May 2025

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Abstract

Farmers sometimes grow crops with low yields, wasting land, labor, and time—especially in developing countries where demand for food is increasing. A Crop Recommendation System (CRS) can help by using precision farming techniques that analyze soil and environmental data to suggest the most suitable crops. This study proposes a CRS using a Modified Salp Swarm Algorithm (MSSA) for feature selection and an Adaptive Weighted Bi-directional Long Short-Term Memory (AWBiLSTM) ensemble for prediction. MSSA enhances the original algorithm by improving local search and convergence speed, addressing SSA’s limitations. Climate data is pre-processed and relevant features are selected using MSSA. AWBiLSTM then predicts suitable crops with improved accuracy. Experimental results show that the MSSA-AWBiLSTM approach outperforms existing methods in precision, recall, and execution time. The proposed method obtains an accuracy of 98.72%, precision of 98.81%, recall of 98.54%, specificity of 98.10%, PR-Score of 98.18%, ROC-Score of 98.49%, F1-Score of 98.48%, and Matthews Correlation Coefficient (MCC) of 97.63%.

Graphical Abstract

Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation

Keywords

soil and climate conditions

feature Selection

crop recommendation

modified salp swarm algorithm

adaptive weighting based BiLSTM

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The author declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

References

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Majumdar, P. (2025). Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation. ICCK Transactions on Swarm and Evolutionary Learning, 1(1), 3–11. https://doi.org/10.62762/TSEL.2025.947593

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TY  - JOUR
AU  - Majumdar, Parijata
PY  - 2025
DA  - 2025/05/29
TI  - Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation
JO  - ICCK Transactions on Swarm and Evolutionary Learning
T2  - ICCK Transactions on Swarm and Evolutionary Learning
JF  - ICCK Transactions on Swarm and Evolutionary Learning
VL  - 1
IS  - 1
SP  - 3
EP  - 11
DO  - 10.62762/TSEL.2025.947593
UR  - https://www.icck.org/article/abs/TSEL.2025.947593
KW  - soil and climate conditions
KW  - feature Selection
KW  - crop recommendation
KW  - modified salp swarm algorithm
KW  - adaptive weighting based BiLSTM
AB  - Farmers sometimes grow crops with low yields, wasting land, labor, and time—especially in developing countries where demand for food is increasing. A Crop Recommendation System (CRS) can help by using precision farming techniques that analyze soil and environmental data to suggest the most suitable crops. This study proposes a CRS using a Modified Salp Swarm Algorithm (MSSA) for feature selection and an Adaptive Weighted Bi-directional Long Short-Term Memory (AWBiLSTM) ensemble for prediction. MSSA enhances the original algorithm by improving local search and convergence speed, addressing SSA’s limitations. Climate data is pre-processed and relevant features are selected using MSSA. AWBiLSTM then predicts suitable crops with improved accuracy. Experimental results show that the MSSA-AWBiLSTM approach outperforms existing methods in precision, recall, and execution time. The proposed method obtains an accuracy of 98.72%, precision of 98.81%, recall of 98.54%, specificity of 98.10%, PR-Score of 98.18%, ROC-Score of 98.49%, F1-Score of 98.48%, and Matthews Correlation Coefficient (MCC) of 97.63%.
SN  - 3069-2962
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Majumdar2025Modified,
  author = {Parijata Majumdar},
  title = {Modified Salp Swarm Algorithm with Adaptive Weighting Based Bidirectional LSTM Network Ensemble Method for Crop Recommendation},
  journal = {ICCK Transactions on Swarm and Evolutionary Learning},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {3-11},
  doi = {10.62762/TSEL.2025.947593},
  url = {https://www.icck.org/article/abs/TSEL.2025.947593},
  abstract = {Farmers sometimes grow crops with low yields, wasting land, labor, and time—especially in developing countries where demand for food is increasing. A Crop Recommendation System (CRS) can help by using precision farming techniques that analyze soil and environmental data to suggest the most suitable crops. This study proposes a CRS using a Modified Salp Swarm Algorithm (MSSA) for feature selection and an Adaptive Weighted Bi-directional Long Short-Term Memory (AWBiLSTM) ensemble for prediction. MSSA enhances the original algorithm by improving local search and convergence speed, addressing SSA’s limitations. Climate data is pre-processed and relevant features are selected using MSSA. AWBiLSTM then predicts suitable crops with improved accuracy. Experimental results show that the MSSA-AWBiLSTM approach outperforms existing methods in precision, recall, and execution time. The proposed method obtains an accuracy of 98.72\%, precision of 98.81\%, recall of 98.54\%, specificity of 98.10\%, PR-Score of 98.18\%, ROC-Score of 98.49\%, F1-Score of 98.48\%, and Matthews Correlation Coefficient (MCC) of 97.63\%.},
  keywords = {soil and climate conditions, feature Selection, crop recommendation, modified salp swarm algorithm, adaptive weighting based BiLSTM},
  issn = {3069-2962},
  publisher = {Institute of Central Computation and Knowledge}
}

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