Smart farming may be defined as a farming practise that uses the thought process of modern technology to increase the number and quality of agricultural products. Edge-based smart farming is a system designed solely for the monitoring of crops in the field using sensors and automating the irrigation system to meet our demands. Old cloud-based systems that rely heavily on IoT models are incapable of handling traffic and also knowledge information. As a result, there seems to be reduced latency, longer battery life for IoT devices, lots of cost-effective money-based information management, access to knowledge management and AI, cubic centimetres. So, in this paper we propose an algorithm Ensembled Enabled Edge computing algorithm for developing a smart farming system for crop recommendation in better way for getting best result and compared with some of the existing base classifiers like SVM and Naïve-Bayes in which the proposed algorithm gave the best accuracy when compared with the exisiting classifiers i.e., 91%.
E. Said Mohamed, AA. Belal, S. Kotb Abd-Elmabod, M. A. El-Shirbeny, A. Gad, and M. B. Zahran, “Smart farming for improving agricultural management,” The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, pp. 971–981, Dec. 2021, doi: 10.1016/j.ejrs.2021.08.007.
Y. Tang, S. Dananjayan, C. Hou, Q. Guo, S. Luo, and Y. He, “A survey on the 5G network and its impact on agriculture: Challenges and opportunities,” Computers and Electronics in Agriculture, vol. 180, p. 105895, Jan. 2021, doi: 10.1016/j.compag.2020.105895.
M.E.S. Said, A. Ali, M. Borin, S.K. Abd-Elmabod, A.A. Aldosari, M. Khalil, M.K. Abdel-Fattah On the use of multivariate analysis and land evaluation for potential agricultural development of the northwestern coast of EgyptAgronomy, 10 (9) (2020), p. 1318.
B. Darwin, P. Dharmaraj, S. Prince, D. E. Popescu, and D. J. Hemanth, “Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review,” Agronomy, vol. 11, no. 4, p. 646, Mar. 2021, doi: 10.3390/agronomy11040646.
G. Adamides et al., “Smart Farming Techniques for Climate Change Adaptation in Cyprus,” Atmosphere, vol. 11, no. 6, p. 557, May 2020, doi: 10.3390/atmos11060557.
M. K. Abdel-Fattah, S. K. Abd-Elmabod, A. A. Aldosari, A. S. Elrys, and E. S. Mohamed, “Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta,” Water, vol. 12, no. 9, p. 2537, Sep. 2020, doi: 10.3390/w12092537.
M. K. Abdel-Fattah et al., “Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt,” Sustainability, vol. 13, no. 4, p. 1824, Feb. 2021, doi: 10.3390/su13041824.
S.K. Abd-Elmabod, H. Mansour, A.A.E.F. Hussein, E.S. Mohamed, Z. Zhang, M. Anaya-Romero, A. Jordán, “Influence of irrigation water quantity on the land capability classification Plant Arch”, 2, pp. 2253-2561, 2019.
R. L. F. Cunha, B. Silva, and M. A. S. Netto, “A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast,” 2018 IEEE 14th International Conference on e-Science (e-Science), pp. 423–430, Oct. 2018, doi: 10.1109/escience.2018.00131.
N. Rathore, R. R. Naik, S. Gautam, R. Nahta, and S. Jain, “Smart Farming Based on IoT-Edge Computing: Exploiting Microservices’ Architecture for Service Decomposition,” Data Management, Analytics and Innovation, pp. 425–437, 2024, doi: 10.1007/978-981-97-3242-5_28.
J. Miao, D. Rajasekhar, S. Mishra, S. K. Nayak, and R. Yadav, “A Fog-based Smart Agriculture System to Detect Animal Intrusion,” 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), Dec. 2023, doi: 10.1109/icpads60453.2023.00336.
C. M. Angelopoulos, G. Filios, S. Nikoletseas, and T. P. Raptis, “Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses,” Computer Networks, vol. 167, p. 107039, Feb. 2020, doi: 10.1016/j.comnet.2019.107039.
G. K. T and K. V. Shashank, “Smart Farming based on AI, Edge Computing and IoT,” 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 324–327, Sep. 2022, doi: 10.1109/icirca54612.2022.9985023.
J. Avanija, C. Rajyalakshmi, K. R. Madhavi, and B. N. K. Rao, “Enabling Smart Farming Through Edge Artificial Intelligence (AI),” Agriculture and Aquaculture Applications of Biosensors and Bioelectronics, pp. 69–82, Apr. 2024, doi: 10.4018/979-8-3693-2069-3.ch004.
M. Cruz, S. Mafra, E. Teixeira, and F. Figueiredo, “Smart Strawberry Farming Using Edge Computing and IoT,” Sensors, vol. 22, no. 15, p. 5866, Aug. 2022, doi: 10.3390/s22155866.
A. X. Wang, C. Tran, N. Desai, D. Lobell, and S. Ermon, “Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data,” Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, pp. 1–5, Jun. 2018, doi: 10.1145/3209811.3212707.
F. Awuor, K. Kimeli, K. Rabah, & D. Rambim, “ICT solution architecture for agriculture,” In IST-Africa Conference and Exhibition (IST-Africa), IEEE, pp. 1-7, May 2013.
L. Bornn and J. V. Zidek, “Efficient stabilization of crop yield prediction in the Canadian Prairies,” Agricultural and Forest Meteorology, vol. 152, pp. 223–232, Jan. 2012, doi: 10.1016/j.agrformet.2011.09.013.
X. Mo, S. Liu, Z. Lin, Y. Xu, Y. Xiang, and T. R. McVicar, “Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain,” Ecological Modelling, vol. 183, no. 2–3, pp. 301–322, Apr. 2005, doi: 10.1016/j.ecolmodel.2004.07.032.
A. Patil, M. Beldar, A. Naik, & S. Deshpande, “Smart farming using Arduino and data mining,” In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 1913-1917). IEEE.
Van Rijmenam, M. (2015). John Deere is revolutionizing farming with Big Data Amazon Web Services (AWS), https://aws.amazon.com.
P. Han, J. Wang, Z. Ma, A. Lu, M. Gao, and L. Pan, “Application of Fuzzy Clustering Analysis in Classification of Soil in Qinghai and Heilongjiang of China,” Computer and Computing Technologies in Agriculture IV, pp. 282–289, 2011, doi: 10.1007/978-3-642-18333-1_33.
J. Chen, H. Huang, S. Tian, & Y. Qu, “Feature selection for text classification with Naïve Bayes. Expert Systems with Applications,” 36(3),5432-5435.
V. Thamilarasi, “A Detection of Weed in Agriculture Using Digital Image Processing, International Journal of Computational Research and Development,” ISSN: 2456 – 3137, pp.72-77, 2017.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Anil Kumar Ghadiyaram and Srinivasa L Chakravarthy;
Methodology: Anil Kumar Ghadiyaram;
Software: Srinivasa L Chakravarthy;
Data Curation: Anil Kumar Ghadiyaram;
Writing- Original Draft Preparation: Anil Kumar Ghadiyaram and Srinivasa L Chakravarthy;
Visualization: Anil Kumar Ghadiyaram;
Investigation: Srinivasa L Chakravarthy;
Supervision: Anil Kumar Ghadiyaram;
Validation: Srinivasa L Chakravarthy;
Writing- Reviewing and Editing: Anil Kumar Ghadiyaram and Srinivasa L Chakravarthy; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr.Srinivasa L Chakravarthy for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Srinivasa L Chakravarthy
Department of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, Andra Pradesh, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Anil Kumar Ghadiyaram and Srinivasa L Chakravarthy, “An Ensemble Classification Model for Crop Recommendation with Edge Computing”, Journal of Machine and Computing, vol.5, no.4, pp. 2056-2065, October 2025, doi: 10.53759/7669/jmc202505160.
