A Comprehensive System for Sustainable Tree Plantation and Growth Monitoring using Blockchain, AI, and IoT
Keywords:
Growth detection, Secured using blockchain, Sustainability, Tracking volunteers, Transparency, Transparent plantation drives, Tree plantation analysisAbstract
There are several environmental challenges faced by the world today, with deforestation and climate change being major threats to the environment and its sustainability. NGOs and Government bodies play a crucial role in addressing these issues by organizing and conducting tree plantation drives. However, a lack of transparency, mismanagement of funds, and inefficient tracking systems, have hindered the effectiveness of these efforts. Many problems occur after tree plantation as there is no record being held to track the growth of trees, funds transparency is not available, no overall analysis is provided for deciding which tree species should be planted in a particular area to achieve maximum sustainability and also to improve the chances of growth of trees. Only planting trees in large numbers won’t help to solve this problem, a proper system is needed which can record time to time data regarding each and every tree through which we can help in the survival of all the trees and increase their lifespan. This will also help us in avoiding the drying and death of trees. The solution that we propose in this paper, to address the existing drawbacks is to create a web3 based platform to ensure the transparency of transferred funds and tree plantation by NGOs and government bodies, along with which we will implement a feature of tracking the status of planted trees using volunteers and IOT device in areas that aren’t easily accessible by volunteers
Downloads
References
S. Aboussaid, H. Benbihi, and Y. Salih Alj, “RFID-based tracking system preventing trees extinction and deforestation,” 2013 4th International Conference on Intelligent Systems, Modelling and Simulation, 2013. doi:10.1109/isms.2013.41 W.-K. Chen, Linear Networks and Systems. Belmont, CA, USA: Wadsworth, 1993, pp. 123–135.
R. F. Keefe, E. G. Zimbelman, and G. Picchi, “Use of individual tree and product level data to improve operational forestry,” Current Forestry Reports, vol. 8, no. 2, pp. 148–165, 2022. doi:10.1007/s40725-022- 00160-3E.
L. A. Wells and W. Chung, “Real-time computer vision for Tree STEM detection and tracking,” Forests, vol. 14, no. 2, p. 267, 2023. doi:10.3390/f14020267
S. Gupta, A. Mudgil, and A. Soni, “Plant Growth Monitoring System,” International Journal of Engineering Research & Technology, vol. 1, no. 4, Jun. 2012.
N. Fahlgren et al., “A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in setaria,” Molecular Plant, vol. 8, no. 10, pp. 1520–1535, 2015. doi:10.1016/j.molp.2015.06.005
S. Samiei, P. Rasti, J. Ly Vu, J. Buitink, and D. Rousseau, “Deep learning-based detection of seedling development,” Plant Methods, vol. 16, no. 1, 2020. doi:10.1186/s13007-020-00647-9
M. A. Gehan et al., “PLANTCV V2: Image Analysis Software for high-throughput plant phenotyping,” PeerJ, vol. 5, 2017. doi:10.7717/peerj.4088
L. A. Pragasan and N. Ganesan, “Assessment of air pollutants and pollution tolerant tree species for the development of greenbelt at Narasapura Industrial Estate, India,” Geology, Ecology, and Landscapes, pp. 1–9, 2022. doi:10.1080/24749508.2022.2144857
M. J. Kwak et al., “Evaluation of the importance of some East Asian tree species for refinement of air quality by Estimating Air Pollution Tolerance Index, anticipated performance index, and Air Pollutant Uptake,” Sustainability, vol. 12, no. 7, p. 3067, 2020. doi:10.3390/su12073067
A. Almaghrabi and A. Alhogail, “Blockchain-based donations traceability framework,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9442–9454, 2022. doi:10.1016/j.jksuci.2022.09.021
G. Subathra, A. Antonidoss, and B. K. Singh, “Decentralized consensus blockchain and ipfs-based data aggregation for Efficient Data Storage Scheme,” Security and Communication Networks, vol. 2022, pp. 1–13, 2022. doi:10.1155/2022/3167958
A. P. C. W. B. Heinzelman and H. Balakrishnan, “An applicationspecific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, pp. 660–670, 2002.
A. C. W. R. Heinzelman and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proc. The Hawaii International Conference on System Sciences, Hawaii, 2002, pp. 3005–3014.
D. E. B. Krishnamachari and S. Wicker, “Modeling data-centric routing in wireless sensor networks,” Wireless Communications, vol. 1, pp. 660–670, 2002.
V. Godbole, “Performance analysis of antnet-la protocol for ad-hoc networks based on disaster area mobility model,” Radio Electronics Society, Vietnam (REV) Journal of Electronics and Communications, vol. 3, no. 1-2, pp. 28–39, 2013.
J. S. Baras and H. Mehta, “A probabilistic emergent routing algorithm for mobile ad hoc networks,” 2003.
D. E. Goldberg, Genetic Algorithm in a Search Optimization and Machine Learning. Ad- dison Wesley, Boston, 1989.
G. D. Caro and M. Dorigo, “Mobile agents for adaptive routing,” in proc. The Thirty-First Hawaii International Conference on System Sciences, august 1998, pp. 74–83.
V. Godbole, “Performance analysis of bio-inspired routing protocols based on random way- point mobility model,” Defence S & T Technical Bulletin, vol. 2, pp. 114–134.
P. Lalbakhsh, B. Zaeri, and M. N. Fesharaki, “Applying nonlinear learning scheme on antnet routing algorithm,” in Proc. Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2010, pp. 1–6.
D. E. Knuth and D. Bibby, The texbook. Addison-Wesley Reading, MA, USA, 1986, vol. 1993
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
