Data Analytics in Farming: Rice price prediction in Andhra Pradesh

Reddy, Pundru Chandra Shaker; Suryanarayana, G; K, LNC Prakash; Sucharitha, Yadala

doi:10.1109/impact55510.2022.10029009

Cited by 26 publications

(2 citation statements)

References 23 publications

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“…In AP, time-series data analytics are still utilized to forecast rice prices, such as machine learning algorithms. Prototypes of SARIMA, Holt-Winter's Periodic technique [5], and Long Short-Term Memory Neural Networks were implemented. Based on the RMSE value on the rice database, which included expenses from 2001 to 2020, their performance was assessed.…”

Section: Literature Surveymentioning

confidence: 99%

Zone-II & III: Machine Learning based Rice Yield Prediction in Andhra Pradesh

Ramisetty,

Bansode,

Atmakur

et al. 2024

MATEC Web Conf.

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In a unique approach, this research predicts rice yield in Zones II and III of Andhra Pradesh using machine learning algorithms. Because food security and agricultural sustainability are becoming more and more important, an accurate assessment of crop yields is necessary for agricultural planning and decision-making. By using machine learning approaches, we hope to develop robust prediction models that can forecast rice yields depending on a variety of agronomic and environmental conditions. Our plan includes collecting a lot of data on variables such as soil qualities, weather patterns, crop management practices, and historical yield records from the target regions. Using careful feature engineering and data preprocessing, we find relevant predictors and derive significant insights for rice yield estimation. Next, we employ state-of-the-art machine learning methods, like Support Vector Machines, Random Forest, and Gradient Boosting, to build trustworthy prediction models. These models are trained on historical data and then cross-validated to guarantee accuracy and generalizability. Our goal is to combine domain expertise in agriculture with cutting-edge machine learning techniques to provide farmers, policymakers, and other agricultural stakeholders with insightful understandings of the dynamics of rice production. In Zones II and III of Andhra Pradesh, this will make it easier to allocate resources and make improve agricultural output and well-informed decisions sustainability.

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Section: Literature Surveymentioning

confidence: 99%

Zone-II & III: Machine Learning based Rice Yield Prediction in Andhra Pradesh

Ramisetty,

Bansode,

Atmakur

et al. 2024

MATEC Web Conf.

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“…Te use of these smart grid characteristics to grid-connected microgrids has considerable promise. If this system is implemented successfully, it can serve as an example of sustainable development for many regions of Asia, where almost 40% of the population lacks access to power [15][16][17][18][19][20]. In this study, PowerFlexHouse, a research center for investigating the technical possibilities of active load control in a distributed power system with a high penetration of renewable energy, is introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Controller Design and Fault Prediction Using Machine Learning Model

Kumar

Pande

Kumar

et al. 2023

International Transactions on Electrical Energy Systems

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In a solar power plant, a solid phase transformer and an optimization coordinated controller are utilized to improve transient responsiveness. Transient stability issues in a contemporary electrical power system represent one of the difficult tasks for an electrical engineer due to the rise in uncertain renewable energy sources (RESs) as a result of the need for green energy. The potential for terminal voltage to be adversely impacted by this greater RES raises the possibility of electrical device damage. It is possible to use a solid state transformer (SST) or smart transformer to address a transient response issue. These devices are frequently employed to interact between RES and a power grid. SST features a variety of regulated converters to maintain the necessary voltage levels. This method can therefore simultaneously lessen power fluctuations and transient responsiveness. In order to improve the quality of RES power injections and the electrical system’s transient stability, this work provides a controller design for a solar photovoltaic (SPV) system that is connected to the grid by SST. The optimization of a controller model is proposed by modifying a PI controller taken from a commercial one. With the use of IEEE 39 standard buses, the proposed controller is tested. When evaluating the effectiveness of a suggested controller, it is important to take into account a variety of solar radiation patterns as well as a time delay uncertainty that can range from 425 ms to 525 ms. According to simulation results, the proposed controller can be employed to lessen power fluctuation brought on by unpredictable RES. Additionally, the proposed coordinated regulation of SPV and SST can prevent catastrophic damage in the event of substantial disturbances like a circuit breaker collapsing to expand a power line due to a fault by inhibiting significant voltage cycles within an electronic appliance’s rated voltage limit. The results indicate that a transitory stability issue in a modern power system caused by an unforeseen increase in RES may be addressed utilizing the suggested controllers as alternatives.

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