Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning

Authors

  • Subbu Raman Gopi Department of Computer and Information Science, Faculty of Science, Annamalai University, India
  • Mani Karthikeyan Department of Computer and Information Science, Faculty of Science, Annamalai University, India
Volume: 13 | Issue: 4 | Pages: 11360-11365 | August 2023 | https://doi.org/10.48084/etasr.6092

Abstract

Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R2 score of 98.82% and an accuracy of 99.67%.

Keywords:

agriculture, crop yield prediction, crop recommendation, machine learning, moth flame optimizer

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References

R. Jeevaganesh, D. Harish, and B. Priya, "A Machine Learning-based Approach for Crop Yield Prediction and Fertilizer Recommendation," in 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, Apr. 2022, pp. 1330–1334.

P. Muruganantham, S. Wibowo, S. Grandhi, N. H. Samrat, and N. Islam, "A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing," Remote Sensing, vol. 14, no. 9, Jan. 2022, Art. no. 1990.

G. Wen, B. L. Ma, A. Vanasse, C. D. Caldwell, H. J. Earl, and D. L. Smith, "Machine learning-based canola yield prediction for site-specific nitrogen recommendations," Nutrient Cycling in Agroecosystems, vol. 121, no. 2, pp. 241–256, Dec. 2021.

D. Elavarasan, D. R. Vincent P M, K. Srinivasan, and C.-Y. Chang, "A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling," Agriculture, vol. 10, no. 9, Sep. 2020, Art. no. 400.

M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin, and N. Khan, "A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction," IEEE Access, vol. 9, pp. 63406–63439, 2021.

P. Akulwar, "A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach," in Recommender System with Machine Learning and Artificial Intelligence, John Wiley & Sons, Ltd, 2020, pp. 141–163.

M. Gupta, N. Nithin Kumar, M. Reddy, M. Rajasekhar, M. S. Kiran, and M. B. Rohith Reddy, "Smart Use of Solar Radiation in Agriculture Purpose," in 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, Jan. 2023, pp. 1–8.

D. A. Reddy, B. Dadore, and A. Watekar, "Crop Recommendation System to Maximize Crop Yield in Ramtek region using Machine Learning," International Journal of Scientific Research in Science and Technology, vol. 6, no. 1, pp. 485–489, Feb. 2019.

R. Jadhav and D. P. Bhaladhare, "A Machine Learning Based Crop Recommendation System: A Survey," Journal of Algebraic Statistics, vol. 13, no. 1, pp. 426–430, May 2022.

X. Wang et al., "Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn," European Journal of Agronomy, vol. 123, Feb. 2021, Art. no. 126193.

P. V. D. de Souza, L. P. de Rezende, A. P. Duarte, and G. V. Miranda, "Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10338–10346, Apr. 2023.

S. Nemdili, I. C. Ngaru, and M. Kerfa, "Solar-Wind Hybrid Power Generation System Optimization Using Superconducting Magnetic Energy Storage (SMES)," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9515–9522, Dec. 2022.

M. A. Alsuwaiket, "Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorithms," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9247–9251, Oct. 2022.

M. K and R. R, "Crop Recommendation for Better Crop Yield for Precision Agriculture Using Ant Colony Optimization with Deep Learning Method," Annals of the Romanian Society for Cell Biology, pp. 4783–4794, Apr. 2021.

V. Gautam, N. K. Trivedi, A. Anand, and A. Kumar, "Optimized High Performance Deep Neural Network for Crop Recommendation," in 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), Mar. 2022, pp. 838–843.

S. Palvannan and G. Deepak, "HIAS: Hybrid Intelligence Approach for Soil Classification and Recommendation of Crops," in Electronic Governance with Emerging Technologies, Tampico, Mexico, 2022, pp. 81–94.

A. K. Patel and S. Chatterjee, "Computer vision-based limestone rock-type classification using probabilistic neural network," Geoscience Frontiers, vol. 7, no. 1, pp. 53–60, Jan. 2016.

J. Zhang, W. Xiao, Y. Li, S. Zhang, and Z. Zhang, "Multilayer probability extreme learning machine for device-free localization," Neurocomputing, vol. 396, pp. 383–393, Jul. 2020.

M. S. Shaikh, S. Raj, R. Babu, S. Kumar, and K. Sagrolikar, "A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution," Decision Analytics Journal, vol. 6, Mar. 2023, Art. no. 100182.

"Crop Recommendation Dataset." https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset.

"Crops prediction (Indian dataset)." https://kaggle.com/code/prasadkevin/crops-prediction-indian-dataset.

R. Dash, D. K. Dash, and G. C. Biswal, "Classification of crop based on macronutrients and weather data using machine learning techniques," Results in Engineering, vol. 9, Mar. 2021, Art. no. 100203.

W.-T. Zhang, M. Wang, J. Guo, and S.-T. Lou, "Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data," Remote Sensing, vol. 13, no. 14, Jan. 2021, Art. no. 2749.

P. S. Maya Gopal and R. Bhargavi, "A novel approach for efficient crop yield prediction," Computers and Electronics in Agriculture, vol. 165, Oct. 2019, Art. no. 104968.

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How to Cite

[1]
S. R. Gopi and M. Karthikeyan, “Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 4, pp. 11360–11365, Aug. 2023.

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