Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India.
Many tasks are part of smart farming, including predicting crop yields, analysing soil fertility, making crop recommendations, managing water, and many more. In order to execute smart agricultural tasks, researchers are constantly creating several Machine Learning (ML) models. In this work, we integrate ML with the Internet of Things. Either the UCI dataset or the Kaggle dataset was used to gather the data. Effective data pretreatment approaches, such as the Imputation and Outlier (IO) methods, are necessary to manage the intricacies and guarantee proper analysis when dealing with data that exhibits irregular patterns or contains little changes that can have a substantial influence on analysis and decision making. The goal of this research is to provide a more meaningful dataset by investigating data preparation approaches that are particular to processing data. Following the completion of preprocessing, the data is classified using an average approach based on the Ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Neural Network (PNN), and Clustering-Based Decision Tree (CBDT) techniques. The next step in optimising the hyperparameter tuning of the proposed ensemble classifier is to employ a new Tree-Structured Parzen Estimator (TPE). Applying the suggested TPE based Ensemble classification method resulted in a 99.4 percent boost in accuracy
R. Alfred, J. H. Obit, C. P.-Y. Chin, H. Haviluddin, and Y. Lim, “Towards Paddy Rice Smart Farming: A Review on Big Data, Machine
Learning, and Rice Production Tasks,” IEEE Access, vol. 9, pp. 50358–50380, 2021, doi: 10.1109/access.2021.3069449.
E. M. B. M. Karunathilake, A. T. Le, S. Heo, Y. S. Chung, and S. Mansoor, “The Path to Smart Farming: Innovations and Opportunities in
Precision Agriculture,” Agriculture, vol. 13, no. 8, p. 1593, Aug. 2023, doi: 10.3390/agriculture13081593.
S. I. Saleem, S. R. M. Zeebaree, D. Q. Zeebaree and A. M. Abdulazeez, “Building Smart Cities Applications based on IoT Technologies: A
Review,” Technology Reports of Kansai University, vol. 62, no. 3, pp. 1083-1092, 2020.
A. Zervopoulos et al., “Wireless Sensor Network Synchronization for Precision Agriculture Applications,” Agriculture, vol. 10, no. 3, p. 89,
Mar. 2020, doi: 10.3390/agriculture10030089.
D. R. Vincent, N. Deepa, D. Elavarasan, K. Srinivasan, S. H. Chauhdary, and C. Iwendi, “Sensors Driven AI-Based Agriculture
Recommendation Model for Assessing Land Suitability,” Sensors, vol. 19, no. 17, p. 3667, Aug. 2019, doi: 10.3390/s19173667.
W.-S. Kim, W.-S. Lee, and Y.-J. Kim, “A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation,” Journal
of Biosystems Engineering, vol. 45, no. 4, pp. 385–400, Nov. 2020, doi: 10.1007/s42853-020-00078-3.
O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of Things for the Future of Smart Agriculture: A Comprehensive
Survey of Emerging Technologies,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 718–752, Apr. 2021, doi:
10.1109/jas.2021.1003925.
H. K. Adli et al., “Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review,” Sensors, vol. 23, no. 7, p.
3752, Apr. 2023, doi: 10.3390/s23073752.
K. Paul et al., “Viable smart sensors and their application in data driven agriculture,” Computers and Electronics in Agriculture, vol. 198, p.
107096, Jul. 2022, doi: 10.1016/j.compag.2022.107096.
M. Brambilla et al., “From Conventional to Precision Fertilization: A Case Study on the Transition for a Small-Medium Farm,”
AgriEngineering, vol. 3, no. 2, pp. 438–446, Jun. 2021, doi: 10.3390/agriengineering3020029.
D. Radočaj, M. Jurišić, M. Gašparović, I. Plaščak, and O. Antonić, “Cropland Suitability Assessment Using Satellite-Based Biophysical
Vegetation Properties and Machine Learning,” Agronomy, vol. 11, no. 8, p. 1620, Aug. 2021, doi: 10.3390/agronomy11081620.
S. Gokool et al., “Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A
Scoping Review and Bibliometric Analysis,” Sustainability, vol. 15, no. 4, p. 3557, Feb. 2023, doi: 10.3390/su15043557.
S. Parez, N. Dilshad, N. S. Alghamdi, T. M. Alanazi, and J. W. Lee, “Visual Intelligence in Precision Agriculture: Exploring Plant Disease
Detection via Efficient Vision Transformers,” Sensors, vol. 23, no. 15, p. 6949, Aug. 2023, doi: 10.3390/s23156949.
S. B. Kasturi, CH. Ellaji, D. Ganesh, K. Somasundaram and B. Sreedhar, “IoT and Machine Learning Approaches for Classification in
Smart Farming,” Journal of Survey in Fisheries Sciences, vol. 10, no. 4S, pp. 3373-3385, 2023.
A. Haldorai, B. Lincy R, S. M, and M. Balakrishnan, “An improved single short detection method for smart vision-based water garbage
cleaning robot,” Cognitive Robotics, vol. 4, pp. 19–29, 2024, doi: 10.1016/j.cogr.2023.11.002.
A. Gupta and P. Nahar, “Classification and yield prediction in smart agriculture system using IoT,” Journal of Ambient Intelligence and
Humanized Computing, vol. 14, no. 8, pp. 10235–10244, Jan. 2022, doi: 10.1007/s12652-021-03685-w.
H. Alshahrani et al., “Chaotic Jaya Optimization Algorithm With Computer Vision-Based Soil Type Classification for Smart Farming,”
IEEE Access, vol. 11, pp. 65849–65857, 2023, doi: 10.1109/access.2023.3288814.
P. Kathiria, U. Patel, S. Madhwani, and C. S. Mansuri, “Smart Crop Recommendation System: A Machine Learning Approach for Precision
Agriculture,” Machine Intelligence Techniques for Data Analysis and Signal Processing, pp. 841–850, 2023, doi: 10.1007/978-981-99-
0085-5_68.
Y. Akkem, S. K. Biswas, and A. Varanasi, “Smart Farming Monitoring Using ML and MLOps,” Lecture Notes in Networks and Systems,
pp. 665–675, 2023, doi: 10.1007/978-981-99-3315-0_51.
I. V. Mboweni, D. T. Ramotsoela, and A. M. Abu-Mahfouz, “Hydraulic Data Preprocessing for Machine Learning-Based Intrusion
Detection in Cyber-Physical Systems,” Mathematics, vol. 11, no. 8, p. 1846, Apr. 2023, doi: 10.3390/math11081846.
S. R. B. S, M. G, and E. Sherly, “Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed
Algorithm,” 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS), Feb. 2023, doi:
10.1109/aicaps57044.2023.10074562.
R. Nagi and S. S. Tripathy, “Plant disease identification using fuzzy feature extraction and PNN,” Signal, Image and Video Processing, vol.
17, no. 6, pp. 2809–2815, Jan. 2023, doi: 10.1007/s11760-023-02499-x.
A. K. Thakur, A. Mukherjee, P. K. Kundu, and A. Das, “Classification and Authentication of Induction Motor Faults using Time and
Frequency Feature Dependent Probabilistic Neural Network Model,” Journal of The Institution of Engineers (India): Series B, vol. 104, no.
3, pp. 623–640, Mar. 2023, doi: 10.1007/s40031-023-00872-5.
A. B. Tufail et al., “3D convolutional neural networks-based multiclass classification of Alzheimer’s and Parkinson’s diseases using PET
and SPECT neuroimaging modalities,” Brain Informatics, vol. 8, no. 1, Nov. 2021, doi: 10.1186/s40708-021-00144-2.
J. Liang et al., “Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured p arzen estimators,”
IET Collaborative Intelligent Manufacturing, vol. 4, no. 3, pp. 194–207, Sep. 2022, doi: 10.1049/cim2.12055.
S. Watanabe, N. Awad, M. Onishi and F. Hutter, “Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based
Meta-Learning for the Tree-Structured Parzen Estimator,” arXiv 2022, arXiv:2212.06751.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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
Balamanigandan R
Balamanigandan R
Department of Computer Science and Engineering, Saveetha College of Engineering, SIMATS, Chennai, 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
Latha M, Mandadi Vasavi, Chunduri Kiran Kumar, Balamanigandan R, John Babu Guttikonda and Rajesh Kumar T, “Machine Learning Based Precision Agriculture using Ensemble Classification with TPE Model”, Journal of Machine and Computing, pp. 261-268, January 2024. doi: 10.53759/7669/jmc202404025.
