Smart City Gnosys
Smart city article details
| Title | Location And Weather-Based Prediction Of Yields Using Machine Learning Algorithms In Smart Cities |
|---|---|
| ID_Doc | 35500 |
| Authors | Amuthasurabi M.; Maaran C.T.; Vyas H.; Prakash R.K.; Arvindhan S.P.; Ashwin B. |
| Year | 2025 |
| Published | Deep Learning and Blockchain Technology for Smart and Sustainable Cities |
| DOI | http://dx.doi.org/10.1201/9781003476047-18 |
| Abstract | As the level of unusual weather conditions increases, the farmers are unable to decide precisely which crop must be sowed to get the maximum yield. So, this chapter aims to mitigate the risks of decision-making by developing an app and integrating it with a predictive model. This chapter endeavors to address the pressing need for precision in agricultural decision-making, particularly regarding crop selection during the sowing season in smart cities. In light of escalating food demands and the unpredictable nature of weather patterns, farmers face mounting challenges in determining the most suitable crops to cultivate in order to meet basic requirements in smart cities. In response, our initiative focuses on developing predictive model aiming to empower. At its core, our predictive model harnesses advanced algorithms to analyze diverse sets of data, including historical crop yields, soil quality metrics, weather forecasts, and market trends in smart cities. By assimilating this wealth of information, the model generates actionable insights tailored to specific geographical regions and seasonal conditions in smart cities. Central to the utility of our solution is its ability to present information in a visually intuitive manner in smart cities. Through dynamic visualization techniques, such as interactive pie charts, farmers gain understanding of the potential yield percentages, which is comprehensive associated with various crop options in smart cities. This visualization not only enhances decision-making capabilities but also fosters greater transparency and engagement among stakeholders in smart cities. Moreover, our predictive model is designed to adapt and evolve over time, leveraging machine learning algorithms to refine the predictions continuously based on real-time data inputs in smart cities. By staying abreast of shifting agricultural dynamics and emerging trends, the model remains relevant and effective in facilitating optimal decision-making processes in smart cities. Ultimately, our chapter endeavors to empower farmers with a robust toolset that enables them for the navigation of complexities of modern agriculture with confidence and foresight in smart cities. By harnessing the power of predictive analytics and visualization technologies, we aim to foster sustainable agricultural practices while enhancing food security and livelihoods in farming communities in smart cities. © 2025 selection and editorial matter, V. Subramaniyaswamy, G Revathy, Logesh Ravi, N. Thillaiarasu, and Naresh Kshetri; individual chapters, the contributors. |
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