Rain Prediction Using Polynomial Regression for the Field of Agriculture Prediction for Karnatakka

Shah, Bhupendra; Thapa, Sudeep Dhoj; Diyali, Ram Sharan; Shashikala, H K; Maharjan, Sis

doi:10.2139/ssrn.3635278

Cited by 3 publications

(5 citation statements)

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“…Regression algorithms play a central role in rice nutrient prediction by unraveling the intricate interplay of nutrients in rice cultivation. Elastic Net Regression (EN), Polynomial Regression (PN), Stepwise Regression (SW), Ridge Regression (RR), Lasso Regression (LS), and Linear Regression (LR) provide essential insights into the complex relationships among soil composition, environmental variables, and agricultural practices [27][28][29][30]. These algorithms empower researchers to comprehend the often-nonlinear dependencies among these factors, deepening our understanding of how various nutrients influence rice nutrition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By harnessing historical data and observational insights, these algorithms provide crucial guidance on how different nutrients impact rice composition. This knowledge is vital for optimizing fertilizer usage, enhancing nutrient management, and ultimately improving rice quality and yields [27][28][29][30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 provides a comparative analysis of the advantages and disadvantages of regression algorithms [26][27][28][29][30][31][32][33][34][35] for rice nutrient prediction. These algorithms effectively capture both linear and nonlinear correlations among various nutrients.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A Concentration Prediction-Based Crop Digital Twin Using Nutrient Co-Existence and Composition in Regression Algorithms

Ghazvini,

Sharef,

Balasundram

et al. 2024

Applied Sciences

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Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Concentration Prediction-Based Crop Digital Twin Using Nutrient Co-Existence and Composition in Regression Algorithms

Ghazvini,

Sharef,

Balasundram

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, with the recent advancement of machine learning, many new machine learning-based models have been proposed for rainfall prediction. Researchers like Shah et al [3] have developed a simple polynomial regression-based model to predict the rain to benefit agricultural products. Asha et al [4] have proposed a hybrid machine learning classification model for predicting rainfall, and it has shown better performance than the ordinary ml-based model.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Rainfall Prediction Using Supervised Machine Learning

Prottasha¹,

Tahabilder²,

Kowsher³

et al. 2023

Adv. technol. innov.

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Floods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.

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“…Once the user selects a service category, they should be able to view the details of each service, such as the price, delivery time, and writer's qualifications [12,13]. The system should provide detailed information about each service to help users make informed decisions.…”

Section: Service Detailsmentioning

confidence: 99%

Online Service Booking Platform with Payment Integration

Shashikala

Singh

Kumar

et al. 2023

International Journal of Information Technology, Research and A

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This research work provides the design and development of an online service booking platform with payment integration. The platform is designed to enable users to book and pay for services such as plumbing and electrical services online from the comfort of their own homes. The system uses HTML, CSS, and PHP to create an intuitive user interface, as well as a secure payment gateway for online transactions. The platform is designed to provide better user experience and the system is designed for both mobile and desktop devices. The study concludes by providing a set of recommendations for future development of the platform, and discusses the potential benefits and challenges of online service booking platforms.

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Rain Prediction Using Polynomial Regression for the Field of Agriculture Prediction for Karnatakka

Cited by 3 publications

References 0 publications

A Concentration Prediction-Based Crop Digital Twin Using Nutrient Co-Existence and Composition in Regression Algorithms

A Concentration Prediction-Based Crop Digital Twin Using Nutrient Co-Existence and Composition in Regression Algorithms

Short-Term Rainfall Prediction Using Supervised Machine Learning

Online Service Booking Platform with Payment Integration

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