Abstract
In order to assist farmers in selecting the most suitable crops based on environmental characteristics, this article introduces a novel system for crop recommendation that leverages machine learning techniques, specifically ensemble learning with a voting classifier. A comprehensive analysis of prior research in the field of crop recommendation systems reveals the limitations and challenges of previous approaches, particularly their low accuracy. To address these shortcomings, the proposed system incorporates a voting classifier that amalgamates the performance of various machine learning models, while taking into account the perspectives of all participating models. By harnessing the collective intelligence of these models, this approach aims to mitigate the limitations of previous methods and provide more dependable and precise crop recommendations. The results demonstrate the system’s capacity to generate highly accurate recommendations, with the ensemble learning approach achieving an accuracy rate of 99.31%. This empowers farmers to optimize their agricultural practices and maximize crop yields, enabling them to make informed decisions for sustainable and efficient farming.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hosseinzadeh, M., Samadi Foroushani, M., Sadraei, R.: Dynamic performance development of entrepreneurial ecosystem in the agricultural sector. Br. Food J. 124, 2361–2395 (2022)
Mancer, M., Terrissa, L., Ayad, S., Laouz, H.: A Blockchain-based approach to securing data in smart agriculture. In: 2022 International Symposium on Innovative Informatics of Biskra (ISNIB), pp. 1–5 (2022)
Abbasi, R., Martinez, P., Ahmad, R.: The digitization of agricultural industry-a systematic literature review on agriculture 4.0. Smart Agric. Technol. 2, 100042 (2022)
Mathew, J., Joy, A., Sasi, D., Jiji, J., John, J.: Crop prediction and plant disease detection using IoT and machine learning. In: 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 560–565 (2022)
Mancer, M., et al.: Blockchain technology for secure shared medical data. In: 2022 International Arab Conference on Information Technology (ACIT), pp. 1–6 (2022)
Shariff, S., Shwetha, R., Ramya, O., Pushpa, H., Pooja, K.: Crop recommendation using machine learning techniques. Int. J. Eng. Res. Technol. (IJERT) (2022)
Pande, S., Ramesh, P., Anmol, A., Aishwarya, B., Rohilla, K., Shaurya, K.: Crop recommender system using machine learning approach. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1066–1071 (2021)
Bakthavatchalam, K., et al.: IoT framework for measurement and precision agriculture: predicting the crop using machine learning algorithms. Technologies 10, 13 (2022)
Garanayak, M., Sahu, G., Mohanty, S., Jagadev, A.: Agricultural recommendation system for crops using different machine learning regression methods. Int. J. Agric. Environ. Inf. Syst. (IJAEIS) 12, 1–20 (2021)
Alasadi, S., Bhaya, W.: Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12, 4102–4107 (2017)
Majumdar, J., Naraseeyappa, S., Ankalaki, S.: Analysis of agriculture data using data mining techniques: application of big data. J. Big Data 4, 20 (2017)
Alexandropoulos, S., Kotsiantis, S., Vrahatis, M.: Data preprocessing in predictive data mining. Knowl. Eng. Rev. 34, e1 (2019)
Li, Y., Yang, Y., Che, J., Zhang, L.: Predicting the number of nearest neighbor for kNN classifier. IAENG Int. J. Comput. Sci. 46, 662–669 (2019)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach. Learn. 36, 105–139 (1999)
Remadna, I., Terrissa, L., Al Masry, Z., Zerhouni, N.: RUL prediction using a fusion of attention-based convolutional variational autoencoder and ensemble learning classifier. IEEE Trans. Reliab. 72, 106–124 (2023)
Chen, T., et al.: XGBoost: extreme gradient boosting. R Package Version 0.4-2 1, 1–4 (2015)
Hancock, J., Khoshgoftaar, T.: CatBoost for big data: an interdisciplinary review. J. Big Data 7, 1–45 (2020)
Murtagh, F.: Multilayer perceptrons for classification and regression. Neurocomputing 2, 183–197 (1991)
Sharaff, A., Gupta, H.: Extra-tree classifier with metaheuristics approach for email classification. In: Advances in Computer Communication and Computational Sciences: Proceedings of IC4S 2018, pp. 189–197 (2019)
Schaffer, C.: Selecting a classification method by cross-validation. Mach. Learn. 13, 135–143 (1993)
Ingle, A.: Crop recommendation dataset. Kaggle, December 2020. www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mancer, M., Terrissa, L.S., Ayad, S., Laouz, H., Zerhouni, N. (2024). Advancing Crop Recommendation Systems Through Ensemble Learning Techniques. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-54376-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54375-3
Online ISBN: 978-3-031-54376-0
eBook Packages: EngineeringEngineering (R0)