A Review of AI in Precision Agriculture
DOI:
https://doi.org/10.61841/fzb57s25Keywords:
New cultivating, Developing populace, Agribusiness, Misfortunes, UtilizationAbstract
Horticulture accommodates the most fundamental necessities of mankind: food and fiber. The presentation of new cultivating methods in the previous hundred years (e.g., during the Green Upset) has helped farming stay up with developing requests for food and other horticultural items. Notwithstanding, further increments in food interest, a developing populace, and rising pay levels are probably going to overwhelm regular assets. With developing acknowledgment of the adverse consequences of agribusiness on the climate, new procedures and approaches ought to be capable of meeting future food requests while diminishing the ecological impression of horticulture. Arising advancements, for example, geospatial innovations, the Web of Things (IoT), huge information investigation, and man-made consciousness (simulated intelligence), could be used to make informed administration choices intended to increment crop creation. Accuracy farming (Dad) involves the use of a set-up of such innovations to streamline farming contributions to increment rural creation and lessen input misfortunes. Utilization of remote detecting advancements for Dad has expanded quickly during the past few years. The exceptional accessibility of high-goal (spatial, phantom, and transient) satellite pictures has advanced the utilization of remote detecting in numerous Dad applications, including crop.
Downloads
References
1. O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, “An overview of internet of things (iot) and
data analytics in agriculture: Benefits and challenges,” IEEE Internet of Things Journal, 2018. [K. Taylor, C.
Griffith, L.
2. Lefort, R., Gaire, M., Compton, T., Wark, D., Lamb, G., Falzon, M., and Trotter, M., “Farming the web
f things,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 12–19, 2013.
3. L. Zhou, N. Chen, Z. Chen, and C. Xing, “Roscc: An efficient remote sensing observation-sharing method based on
cloud computing for soil moisture mapping in precision agriculture,” IEEE Journal of selected topics in applied
Earth Observations and Remote Sensing, vol. 9, no. 12, pp. 5588–5598, 2016.
4. N. D. Lane, S. Bhattacharya, A. Mathur, P. Georgiev, C. Forlivesi, and F. Kawsar, “Squeezing deep learning into
mobile and embedded devices,” IEEE Pervasive Computing, no. 3, pp. 82–88, 2017.
5. Chaudhury, C., Ward, A., Talasaz, A., Ivanov, N. P., Huner, B., Grodzinski, R. V., Patel, J. L. Barron, J. L., “Computer
vision based autonomous robotic system for 3d plant growth measurement,” in 2015 12th Conference on Computer
and Robot Vision (CRV). IEEE, 2015, pp. 290–296.
6. P. Eugster, V. Sundaram, and X. Zhang, “Debugging the internet of things: The case of wireless sensor networks,”
IEEE Software, no. 1, pp. 1–1, 2015.
7. H. Alavi, P. Jiao, W. G. Buttlar, and N. Lajnef, “Internet ofenabled smart cities: State-of-the-art and future
trends,” Measurement, vol. 129, pp. 589–606, 2018.
8. P. P. De Silva and P. A. De Silva, “Ipanera: An industry 4.0 based architecture for distributed soil-less food
production systems,” in Manufacturing & Industrial Engineering Symposium (MIES). IEEE, 2016, pp. 1–5.
9. L. Li, Q. Zhang, and D. Huang, “A review of imaging techniques for plant phenotyping,” Sensors, vol. 14, no. 11,
pp. 20078–20111, 2014.
10. R. Pahuja, H. Verma, and M. Uddin, “A wireless sensor network for greenhouse climate control,” IEEE Pervasive
Computing, no. 2, pp. 49–58, 2013.
11. E. M. López, M. García, M. Schuhmacher, and J. L. Domingo, ‘‘A fuzzy expert system for soil characterization,’’
Environ. Int., vol. 34, no. 7, pp. 950–958, Oct. 2008.
12. M. Li and R. S. Yost, ‘‘Management-oriented modeling: Optimizing nitrogen management with artificial
intelligence,’’ Agricult. Syst., vol. 65, no. 1, pp. 1–27, Jul. 2000.
13. S. Tajik, S. Ayoubi, and F. Nourbakhsh, ‘‘Prediction of soil enzymes activity by digital terrain analysis: Comparing
artificial neural network and multiple linear regression models,’’ Environ. Eng. Sci., vol. 29, no. 8, pp. 798–806,
Aug. 2012. 110220 VOLUME 9, 2021 S. A. Bhat, N.-F. Huang: Big Data and AI Revolution in Precision
Agriculture: Survey and Challenges
14. H. Lal, J. W. Jones, R. M. Peart, and W. D. Shoup, ‘‘FARMSYS—A whole-farm machinery management decision support system,’’ Agricult. Syst., vol. 38, no. 3, pp. 257–273, Jan. 1992.
15. H. Song and Y. He, ‘‘Crop nutrition diagnosis expert system based on artificial neural networks,’’ in Proc. 3rd Int.
Conf. Inf. Technol. Appl. (ICITA), vol. 1, Jul. 2005, pp. 357–362.
16. B. Ji, Y. Sun, S. Yang, and J. Wan, ‘‘Artificial neural networks for rice yield prediction in mountainous regions,’’ J.
Agricult. Sci., vol. 145, no. 3, p. 249, 2007.
17. K. Balleda, D. Satyanvesh, N. V. S. S. P. Sampath, K. T. N. Varma, and P. K. Baruah, ‘‘Agpest: An efficient rule-based expert system to prevent pest diseases of rice & wheat crops,’’ in Proc. IEEE 8th Int. Conf. Intell. Syst.
Control (ISCO), Jan. 2014, pp. 262–268.
18. G. Liu, X. Yang, Y. Ge, and Y. Miao, ‘‘An artificial neural network-based expert system for fruit tree disease and
insect pest diagnosis,’’ in Proc. IEEE Int. Conf. Netw., Sens. Control, Apr. 2006, pp. 1076–1079.
19. M. Y. Munirah and M. Rozlini, ‘‘An expert system development: Its application on diagnosing oyster mushroom
diseases,’’ in Proc. 13th Int. Conf. Control, Automat. Syst. (ICCAS), Oct. 2013, pp. 329–332. M. Branch, ‘‘A multi
layer perceptron neural network trained by invasive weed optimization for potato color image segmentation,’’
Trends Appl. Sci. Res., vol. 7, no. 6, pp. 445–455, 2012.
20. R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid Connected
Solar PV System," 2018 3rd International Conference and Workshops on Recent Advances and Innovations in
Engineering (ICRAIE), pp. 1-4, 2018.
21. R. Kaushik, O. P. Mahela, P. K. Bhatt, B. Khan, S. Padmanaban and F. Blaabjerg, "A Hybrid Algorithm for
Recognition of Power Quality Disturbances," in IEEE Access, vol. 8, pp. 229184-229200, 2020.
22. Kaushik, R. K. "Pragati: Analysis and Case Study of Power Transmission and Distribution." J Adv Res Power
Electro Power Sys 7.2 (2020): 1-3.
23. Kumar, G., Kaushik, M. and Purohit, R. (2018). “Reliability analysis of software with three types of errors and
imperfect debugging using Markov model,” International journal of computer applications in technology, 58(3), p.
241. doi: 10.1504/ijcat.2018.095763.
24. Sharma, R. and Kumar, G. (2017) “Availability improvement for the successive K-out-of-N machining system using standby with multiple working vacations,” International journal of reliability and safety, 11(3/4), p. 256. doi:10.1504/ijrs.2017.089710.
25. Gireesh, K., Manju, K., and Preeti (2016), “Maintenance policies for improving the availability of a software-hardware system,” in 2016 11th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
