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Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
Open accessAbstract
The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Artificial intelligence: a modern approach
(1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 21,974
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
(2012) · Cited by 9,062
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 6,938
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Bayesian Inference in Statistical Analysis.
Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,824
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
Broomhead, D. S., & Lowe, D. (1988) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 3,443
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Big Data in Smart Farming – A review
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.023 · Cited by 3,378
Bayesian Reasoning and Machine Learning
Barber, D. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511804779 · Cited by 1,631
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.05.012 · Cited by 1,577
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges
Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. H. D. N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2844296 · Cited by 1,467
Machine Learning for Internet of Things Data Analysis: A Survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P. M., & Sheth, A. (2017) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1,008
A comprehensive review on automation in agriculture using artificial intelligence
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.05.004 · Cited by 1,005
Internet of Things in agriculture, recent advances and future challenges
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2017.09.007 · Cited by 1,000
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
Machine learning for internet of things data analysis: a survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921
Automated Irrigation System Using a Wireless Sensor Network and GPRS Module
Gutierrez, J., Villa-Medina, J. F., Nieto-Garibay, A., & Porta-Gandara, M. A. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2276487 · Cited by 914
Gaussian Processes for Machine Learning (GPML) Toolbox
Rasmussen, C. E., & Nickisch, H. (2010) · Max Planck Institute for Plasma Physics · doi:10.5555/1756006.1953029 · Cited by 895
Deep Learning for Image-Based Cassava Disease Detection
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01852 · Cited by 833
Future food-production systems: vertical farming and controlled-environment agriculture
Benke, K., & Tomkins, B. (2017) · Sustainability: Science, Practice and Policy · doi:10.1080/15487733.2017.1394054 · Cited by 817
IoT and agriculture data analysis for smart farm
Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.12.011 · Cited by 767
Ensemble Machine Learning: Methods and Applications
(2012) · doi:10.1007/978-1-4419-9326-7 · Cited by 746
Wheat yield prediction using machine learning and advanced sensing techniques
Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.11.018 · Cited by 613
An introduction to computing with neural nets
Lippmann, R. P. (1988) · ACM SIGARCH Computer Architecture News · doi:10.1145/44571.44572 · Cited by 539
Using deep learning to predict soil properties from regional spectral data
Padarian, J., Minasny, B., & McBratney, A. B. (2019) · Geoderma Regional · doi:10.1016/j.geodrs.2018.e00198 · Cited by 451
Types of machine learning algorithms
Oladipupo, T. (2010) · New Advances in Machine Learning · doi:10.5772/9385 · Cited by 443
The Vertical Farm: Feeding the World in the 21st Century
Despommier, D. D. (2010) · Cited by 368
Technology: The Future of Agriculture
King, A. (2017) · Nature · doi:10.1038/544s21a · Cited by 308
Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.01.027 · Cited by 265
Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability
Vincent, D. R., Deepa, N., Elavarasan, D., Srinivasan, K., Chauhdary, S. H., & Iwendi, C. (2019) · Sensors · doi:10.3390/s19173667 · Cited by 215
Dou, X., & Yang, Y. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.03.010 · Cited by 168
Automatic fruit count on coffee branches using computer vision
Ramos, P. J., Prieto, F. A., Montoya, E. C., & Oliveros, C. E. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.03.010 · Cited by 161
A wireless sensors architecture for efficient irrigation water management
Navarro-Hellín, H., Torres-Sánchez, R., Soto, F., Albaladejo-Pérez, C., López, J. A., & Domingo, R. (2014) · Agricultural Water Management · doi:10.1016/j.agwat.2014.10.022 · Cited by 155
New Advances in Machine Learning
Zhang, Y. (2010) · InTech eBooks · doi:10.5772/225 · Cited by 144
Support vector machine-based open crop model (SBOCM): Case of rice production in China
Su, Y.-X., Xu, H., & Li-jiao, Y. (2017) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2017.01.024 · Cited by 132
Liao, M.-S., Chen, S., Chou, C., Chen, H., Yeh, S.-H., Chang, Y.-C., & Jiang, J. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.03.003 · Cited by 121
dos Santos, U. J. L., Pessin, G., da Costa, C. A., & Righi, R. D. R. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.010 · Cited by 114
Rodríguez, S., Gualotuña, T., & Grilo, C. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.11.042 · Cited by 88
Data Mining of Agricultural Yield Data: A Comparison of Regression Models
Ruß, G. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03067-3_3 · Cited by 86
Rivera‐Ferre, M. G. (2008) · EMBO Reports · doi:10.1038/embor.2008.196 · Cited by 79
Deep Learning and IoT for Smart Agriculture Using WSN
Varman, S. A. M., Baskaran, A. R., Aravindh, S., & Prabhu, E. (2017) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2017.8524140 · Cited by 76
Artificial Neural Network Model as a Data Analysis Tool in Precision Farming
Ирмак, А., Jones, J. W., Batchelor, W. D., Irmak, S., Boote, K. J., & Paz, J. O. (2006) · Transactions of the ASABE · doi:10.13031/2013.22264 · Cited by 58
Thoreau: A subterranean wireless sensing network for agriculture and the environment
Zhang, X., Andreyev, A., Zumpf, C. R., Negri, M. C., Guha, S., & Ghosh, M. (2017) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2017.8116356 · Cited by 50
Mann, M. L., Warner, J. M., & Malik, A. S. (2019) · Climatic Change · doi:10.1007/s10584-019-02432-7 · Cited by 45
Li, Z., Wang, J., Tang, H., Huang, C., Yang, F., Chen, B., Wang, X., Xin, X., & Ge, Y. (2016) · Remote Sensing · doi:10.3390/rs8080632 · Cited by 40
Renewable energy supported microgrid in rural electrification of Sub-Saharan Africa
Mekonnen, Y., & Sarwat, A. I. (2017) · IEEE PES PowerAfrica · doi:10.1109/powerafrica.2017.7991293 · Cited by 39
Self-citationUnmanned aerial system based tomato yield estimation using machine learning
Ashapure, A., Oh, S., Marconi, T. G., et al. (2019) · doi:10.1117/12.2519129 · Cited by 32
A WSN-based prediction model of microclimate in a greenhouse using an extreme learning approach
Liu, Q., Zhang, Y. Y., Shen, J., Xiao, B., & Linge, N. (2015) · International Conference on Advanced Communication Technology · doi:10.1109/icact.2015.7224772 · Cited by 30
An experimental set up for utilizing convolutional neural network in automated weed detection
Tiwari, O., Goyal, V., Kumar, P., et al. (2019) · International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) · doi:10.1109/iot-siu.2019.8777646 · Cited by 30
Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems
Kontogiannis, S. I., Kokkonis, G., Ellinidou, S., & Valsamidis, S. I. (2017) · Future Internet · doi:10.3390/fi9040078 · Cited by 20
Food, Energy and Water (FEW) Nexus Modeling Framework
Mekonnen, Y., Sarwat, A. I., & Bhansali, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-32520-6_28 · Cited by 7
Self-citationA ZigBee Based Architecture for Public Safety Communication in Hurricane Scenario
Parvez, I., Mekonnen, Y., & Sarwat, A. I. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-32523-7_34 · Cited by 2
Self-citation
Cited by
Chaudhary, V., Kaushik, A., Furukawa, H., & Khosla, A. (2022) · ECS Sensors Plus · doi:10.1149/2754-2726/ac5ac6 · Cited by 386
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3075159 · Cited by 369
Metadata
- Authors
- Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S.
- Venue
- Journal of The Electrochemical Society
- Publisher
- The Electrochemical Society
- Published
- 2019-12-19
- Volume
- 167
- Issue
- 3
- Pages
- 037522
- ISSN
- 0013-4651 (ISSN-L)
- 1945-7111
- OpenAlex
- W2996815539
- Cited by
- 282
- References
- 58
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
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