CFP last date
20 May 2024
Reseach Article

A Microclimate based Crop Recommender System for Precision Agriculture

by Al Mustarik, Md. Tawhid Sultan, Md. Mahidul Islam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 3
Year of Publication: 2021
Authors: Al Mustarik, Md. Tawhid Sultan, Md. Mahidul Islam
10.5120/ijca2021921309

Al Mustarik, Md. Tawhid Sultan, Md. Mahidul Islam . A Microclimate based Crop Recommender System for Precision Agriculture. International Journal of Computer Applications. 183, 3 ( May 2021), 41-46. DOI=10.5120/ijca2021921309

@article{ 10.5120/ijca2021921309,
author = { Al Mustarik, Md. Tawhid Sultan, Md. Mahidul Islam },
title = { A Microclimate based Crop Recommender System for Precision Agriculture },
journal = { International Journal of Computer Applications },
issue_date = { May 2021 },
volume = { 183 },
number = { 3 },
month = { May },
year = { 2021 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number3/31908-2021921309/ },
doi = { 10.5120/ijca2021921309 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:46.321471+05:30
%A Al Mustarik
%A Md. Tawhid Sultan
%A Md. Mahidul Islam
%T A Microclimate based Crop Recommender System for Precision Agriculture
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 3
%P 41-46
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Agriculture is not only necessary for producing food but also provides the raw material for other industries, like the fashion industry, dairy industry, sugar industries, etc. Agricultural production is one of the most effective ways to support a country's economy. In this paper, it has been tried to figure out predicting crop production using microclimate data. While doing so, the IoT system has been used here to make this research more efficient and accurate. In this paper, Using IoT devices and machine learning techniques, a crop recommendation model has been proposed. A cloud server-based system has been built to store the machine learning model and a database of previous data readings. This crop recommendation model is beneficial for farmers as well as for researchers. Django Framework has been used to create a web application that shows the predicted results to the farmers based on the machine learning algorithm. And the environment, where the introduction of such a system will reduce farmer risk, save money and time, and reduce the waste of agricultural commodities.

References
  1. Somvanshi, Madan, and Pranjali Chavan. "A review of machine learning techniques using decision tree and support vector machine." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1-7. IEEE, 2016.
  2. Padhy, Neelamadhab, and Rasmita Panigrahi. "Multi relational data mining approaches: A data mining technique." arXiv preprint arXiv:1211.3871 (2012).
  3. Kushwaha, Ashwani Kumar, and Sweta Bhattachrya. "Crop yield prediction using Agro Algorithm in Hadoop." International Journal of Computer Science and Information Technology & Security (IJCSITS) 5, no. 2 (2015): 271-274.
  4. Dahikar, Snehal S., and Sandeep V. Rode. "Agricultural crop yield prediction using artificial neural network approach." International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 2, no. 1 (2014): 683-686.
  5. Ramesh, D., and B. Vishnu Vardhan. "Analysis of crop yield prediction using data mining techniques." International Journal of research in engineering and technology 4, no. 1 (2015): 47-473.
  6. Shakoor, Md Tahmid, et al. "Agricultural production output prediction using supervised machine learning techniques." 2017 1st International Conference on Next Generation Computing Applications (NextComp). IEEE, 2017.
  7. Ahamed, AT M. Shakil, Navid Tanzeem Mahmood, Nazmul Hossain, Mohammad Tanzir Kabir, Kallal Das, Faridur Rahman, and Rashedur M. Rahman. "Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh." In 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 1-6. IEEE, 2015.
  8. Veenadhari, S., Bharat Misra, and C. D. Singh. "Machine learning approach for forecasting crop yield based on climatic parameters." In 2014 International Conference on Computer Communication and Informatics, pp. 1-5. IEEE, 2014.
  9. Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture. 2016 Feb 1; 121:57-65.
  10. Kumar, Rakesh, M. P. Singh, Prabhat Kumar, and J. P. Singh. "Crop Selection Method to maximize crop yield rate using machine learning technique." In 2015 international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM), pp. 138-145. IEEE, 2015.
  11. Chlingaryan, Anna, Salah Sukkarieh, and Brett Whelan. "Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review." Computers and electronics in agriculture 151 (2018): 61-69.
  12. Paswan, Raju Prasad, and Shahin Ara Begum. "Regression and neural networks models for prediction of crop production 1." (2013)
  13. Gandhi, Niketa, Owaiz Petkar, and Leisa J. Armstrong. "Rice crop yield prediction using artificial neural networks." In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp. 105-110. IEEE, 2016.
  14. Kamilaris, Andreas, et al. "Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications." 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). IEEE, 2016.
  15. Lee, Meonghun, Jeonghwan Hwang, and Hyun Yoe. "Agricultural production system based on IoT." 2013 IEEE 16Th international conference on computational science and engineering. IEEE, 2013.
  16. Khattab, Ahmed, Ahmed Abdelgawad, and Kumar Yelmarthi. "Design and implementation of a cloud-based IoT scheme for precision agriculture." 2016 28th International Conference on Microelectronics (ICM). IEEE, 2016
  17. Ryu, Minwoo, et al. "Design and implementation of a connected farm for smart farming system." 2015 IEEE SENSORS. IEEE, 2015.
  18. Rodríguez, Schubert, Tatiana Gualotuña, and Carlos Grilo. "A system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networks." Procedia computer science 121 (2017): 306-313.
  19. Koshy, Santosh Sam, et al. "Application of the internet of things (IoT) for smart farming: a case study on groundnut and castor pest and disease forewarning." CSI Transactions on ICT 6.3-4 (2018): 311-318.
  20. White, Jeffrey W., et al. "Methodologies for simulating impacts of climate change on crop production." Field Crops Research 124.3 (2011): 357-368.
  21. Mearns, Linda O., Cynthia Rosenzweig, and Richard Goldberg. "Effect of changes in interannual climatic variability on CERES-Wheat yields: sensitivity and 2× CO2 general circulation model studies." Agricultural and forest meteorology 62.3-4 (1992): 159-189.
  22. Tubiello, Francesco N., et al. "Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations." European Journal of Agronomy 13.2-3 (2000): 179-189.
  23. Abdullah, Ahsan, et al. "The Case for an Agri Data Warehouse: Enabling Analytical Exploration of Integrated Agricultural Data." Databases and Applications. 2004.
  24. Abdullah, Ahsan, et al. "Learning dynamics of pesticide abuse through data mining." Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation-Volume 32. Australian Computer Society, Inc., 2004.
  25. Veenadhari, S., Bharat Mishra, and C. D. Singh. "Soybean productivity modelling using decision tree algorithms." International Journal of Computer Applications 27.7 (2011): 11-15.
  26. Mestre-Sanchís, Fernando, and María Luisa Feijóo-Bello. "Climate change and its marginalizing effect on agriculture." Ecological economics 68.3 (2009): 896-904.
  27. Tanny, Josef. "Microclimate and evapotranspiration of crops covered by agricultural screens: A review." Biosystems Engineering 114.1 (2013): 26-43.
  28. Zellweger, Florian, Pieter De Frenne, Jonathan Lenoir, Duccio Rocchini, and David Coomes. "Advances in microclimate ecology arising from remote sensing." Trends in Ecology & Evolution 34, no. 4 (2019): 327-341.
  29. Ahmed, Selena, and John Richard Stepp. "Beyond yields: Climate effects on specialty crop quality and agroecological management." Elementa: Science of the Anthropocene 4 (2016): 92.
  30. "Sustainable Development Goals | UNDP". 2021. UNDP. https://www.undp.org/content/undp/en/home/sustainable-development-goals.html.
Index Terms

Computer Science
Information Sciences

Keywords

Microclimate & Precision agriculture Regression Analysis Crop recommendation.