Applications of artificial intelligence in precision agriculture to ameliorate production and distribution

Garimella Bhaskar Narasimha Rao

Article ID: 2374
Vol 4, Issue 2, 2023

VIEWS - 84 (Abstract)

Abstract

Automated intelligence platforms, i.e., machine learning, big data, and Internet of Things (IoT), provide new deployment opportunities within the agricultural marketing paradigm. This study attempts to derive a framework of predictive models to ameliorate crop yield and assists in understanding various features that affect crop yield. On the one hand, it investigates the impact of allied technologies, including networks with memory and generative models, and on the other, it quantitatively analyzes different agri-factors, including the management of plant growth, its quality, crop disease, inorganic fertilizer and pesticide deployment, weed management, irrigation, and field-level phenotyping. Further, the study analyzes the utilization of smart farming and the monitoring of highly dependent variables across the spectrum of precision agriculture. The conclusion is to manifest the importance of networks with memory and generative models and emphasize the vital role of artificial intelligence in transforming farm methods into a novel methodology of smart information communication technology (ICT) in fidelity agriculture. Apart from increased productivity, this study seeks to contribute to the ongoing efforts to reduce the incidence of malnutrition associated with limited access and lower production of food grains.


Keywords

artificial intelligence; machine learning; networks with memory; generative models; smart farming; agri marketing

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References

1. Talaviya T, Shah D, Patel N, et al. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture 2020; 4: 58–73. doi: 10.1016/j.aiia.2020.04.002

2. Elmoulat M, Debauche O, Mahmoudi S, et al. Edge computing and artificial intelligence for landslides monitoring. Procedia Computer Science 2020; 177: 480–487. doi: 10.1016/j.procs.2020.10.066

3. Ampatzidis Y, Partel V, Costa L. Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Computers and Electronics in Agriculture 2020; 174: 105457. doi: 10.1016/j.compag.2020.105457

4. Su WH. Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed control. Artificial Intelligence in Agriculture 2020, 4: 262–271. doi: 10.1016/j.aiia.2020.11.001

5. Heiden B, Alieksieiev V, Volk M, et al. Framing artificial intelligence (AI) additive manufacturing (AM). Procedia Computer Science 2021; 186: 387–394. doi: 10.1016/j.procs.2021.04.161

6. Talukdar S, Pal S, Singha P. Proposing artificial intelligence based livelihood vulnerability index in river islands. Journal of Cleaner Production 2021; 284: 124707. doi: 10.1016/j.jclepro.2020.124707

7. Singh H, Kumar Y. Hybrid artificial chemical reaction optimization algorithm for cluster analysis. Procedia Computer Science 2020; 167: 531–540. doi: 10.1016/j.procs.2020.03.312

8. Aghelpour P, Bahrami-Pichaghchi H, Kisi O. Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Computers and Electronics in Agriculture 2020; 170: 105279. doi: 10.1016/j.compag.2020.105279

9. Riahi Y, Saikouk T, Gunasekaran A, et al. Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications 2021; 173: 114702. doi: 10.1016/j.eswa.2021.114702

10. Yu H, Wen X, Li B, et al. Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China. Computers and Electronics in Agriculture 2020; 176: 105653. doi: 10.1016/j.compag.2020.105653

11. Sukhbaatar S, Szlam A, Weston J, Fergus R. End-to-end memory networks. In: Cortes C, Lawrence N, Lee D, et al. (editors). Advances in Neural Information Processing Systems 28, Proceedings of the Annual Conference on Neural Information Processing Systems; 7–12 December 2015; Montreal, Quebec, Canada. pp. 2440–2448.

12. Weston P, Hong R, Kaboré C, Kull CA. Farmer-managed natural regeneration enhances rural livelihoods in dryland West Africa. Environmental Management 2015; 55(6): 1402–1417. doi: 10.1007/s00267-015-0469-1

13. Soni N, Sharma EK, Singh N, et al. Artificial intelligence in business: From research and innovation to market deployment. Procedia Computer Science 2020; 167: 2200–2210. doi: 10.1016/j.procs.2020.03.272

14. Ahmadi F, Mehdizadeh S, Mohammadi B, et al. Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management 2021; 244: 106622. doi: 10.1016/j.agwat.2020.106622

15. Seyedzadeh A, Maroufpoor S, Maroufpoor E, et al. Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure. Agricultural Water Management 2020; 228: 105905. doi: 10.1016/j.agwat.2019.105905

16. Li X, Liu J, Liu D, et al. Measurement and analysis of regional agricultural water and soil resource composite system harmony with an improved random forest model based on a dragonfly algorithm. Journal of Cleaner Production 2021, 305: 127217. doi: 10.1016/j.jclepro.2021.127217

17. Debauche O, Mahmoudi S, Mahmoudi SA, et al. Edge computing and artificial intelligence for real-time poultry monitoring. Procedia Computer Science 2020; 175: 534–541. doi: 10.1016/j.procs.2020.07.076

18. Bhagat SK, Tung TM, Yaseen ZM. Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. Journal of Cleaner Production 2020; 250: 119473. doi: 10.1016/j.jclepro.2019.119473

19. Liu T, Sun Y, Wang C, et al. Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management. Journal of Cleaner Production 2021; 311: 127546. doi: 10.1016/j.jclepro.2021.127546

20. Kosovic IN, Mastelic T, Ivankovic D. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis. Journal of Cleaner Production 2020; 266: 121489. doi: 10.1016/j.jclepro.2020.121489

21. Maruthi SP, Panigrahi T, Jagannath RPK. Distributed version of hybrid swarm intelligence-Nelder Mead algorithm for DOA estimation in WSN. Expert Systems with Applications 2020; 144: 113112. doi: 10.1016/j.eswa.2019.113112

22. Anastasi S, Madonna M, Monica L. Implications of embedded artificial intelligence—Machine learning on safety of machinery. Procedia Computer Science 2021; 180: 338–343. doi: 10.1016/j.procs.2021.01.171

23. Varela N, Silva J, Pineda OB, et al. Prediction of the corn grains yield through artificial intelligence. Procedia Computer Science 2020; 170: 1017–1022. doi: 10.1016/j.procs.2020.03.080

24. Pathan M, Patel N, Yagnik H, et al. Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture 2020; 4: 81–95. doi: 10.1016/j.aiia.2020.06.001

25. Lu Y, Young S. A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture 2020; 178: 105760. doi: 10.1016/j.compag.2020.105760

26. Zougagh N, Charkaoui A, Echchatbi A. Artificial intelligence hybrid models for improving forecasting accuracy. Procedia Computer Science 2021; 184: 817–822. doi: 10.1016/j.procs.2021.04.013


DOI: https://doi.org/10.54517/ama.v4i2.2374
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