Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming
Abstract
The integration of Internet of Things (IoT) technology in agriculture has transformed traditional farming methods, enabling the emergence of smart agriculture systems. This research paper focuses on utilizing IoT and machine learning techniques to forecast crop yields based on climatic and soil conditions. The study utilizes a dataset sourced from Kaggle, which includes information on 22 distinct crops, such as Maize, Wheat, Mango, Watermelon, and others. The dataset encompasses crucial climatic factors like temperature, humidity, and rainfall, as well as essential soil conditions necessary for optimal crop growth. By employing advanced machine learning algorithms on this dataset, the objective is to develop accurate models capable of predicting crop yields. This, in turn, assists farmers in making well-informed decisions regarding crop management and optimizing agricultural productivity. The outcomes of this research have significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting the implementation of sustainable farming practices.
Keywords
Full Text:
PDFReferences
1. Kanchan P, Shardoor N. Krashignyan: A farmer support system. Asian Journal for Convergence in Technology (AJCT) 2021; 7(3): 1–7. doi: 10.33130/ajct.2021v07i03.001
2. Javed F, Afzal MK, Sharif M, Kim BS. Internet of things (IoT) operating systems support, networking technologies, applications, and challenges: a comparative review. IEEE Communication Surveys and Tutorials 2018; 20(3): 2062–2100.
3. Issad HA, Aoudjit R, Rodrigues JJPC. A comprehensive review of data mining techniques in smart agriculture. Engineering in Agriculture, Environment and Food 2019; 12(4): 511–525. doi: 10.1016/j.eaef.2019.11.003
4. Flak J. Technologies for sustainable biomass supply-overview of market offering. Agronomy 2020; 10(6): 798. doi: 10.3390/agronomy10060798
5. Kavita PM. Satellite-based crop yield prediction using machine learning algorithm. In: Proceedings of the 2021 Asian Conference on Innovation in Technology (ASIANCON 2021); 27–29 August 2021; PUNE, India. pp. 1466–1470.
6. Mupangwa W, Makanza R, Chipindu L, et al. Temporal rainfall trend analysis in different agro-ecological regions of southern Africa. Water SA 2021; 47(4): 466–479.
7. Gado Djibo A, Karambiri H, Seidou O, et al. Linear and non-linear approaches for statistical seasonal rainfall forecast in the Sirba watershed region (SAHEL). Climate 2015; 3(3): 727–752. doi: 10.3390/cli3030727
8. Jain N, Kumar A, Garud S, et al. Crop selection method based on various environmental factors using machine learning. International Research Journal of Engineering and Technology (IRJET) 2017; 4(2): 1530–1533.
9. Wankhede DS. Analysis and prediction of soil nutrients pH, N, P, K for crop using machine learning classifier: A review. In: Proceedings of the International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2020); 23–23 January 2020; Lalitpur, Nepal. pp. 111–121.
10. Bhojwani Y, Singh R, Reddy R, Perumal B. Crop selection and IoT based monitoring system for precision agriculture. In: Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE); 24–25 February 2020; Vellore, India.
11. Majumdar P, Mitra S, Bhattacharya D. IoT for promoting agriculture 4.0: A review from the perspective of weather monitoring, yield prediction, security of WSN protocols, and hardware cost analysis. Journal of Biosystems Engineering 2021; 46(4): 440–461.
12. Imran S. Effective crop selection and conservative irrigation using IoT. International Journal of Science and Research (IJSR) 2016.
13. Rekha P, Rangan VP, Ramesh MV, Nibi KV. High yield groundnut agronomy: An IoT based precision farming framework. In: Proceedings of the 2017 IEEE Global Humanitarian Technology Conference (GHTC); 19-22 October 2017; San Jose, CA, USA. pp. 1–5.
14. Mulge M, Sharnappa M, Sultanpure A, et al. An invitation to subscribe. International Journal of Analytical and Experimental Modal Analysis 2020; 10(1): 1112–1117.
DOI: https://doi.org/10.32629/jai.v7i4.1108
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Rashmi Gera, Anupriya Jain
License URL: https://creativecommons.org/licenses/by-nc/4.0/