Smart Agricultural System

Jadhav, Aishwarya

doi:10.22214/ijraset.2017.10175

Cited by 4 publications

(7 citation statements)

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“…RFID-tag reading does not require manual interactions. The functions and advantages provided to the library with the utmost care using RFID technology include the reducing of manual involvement, reduced manual errors, and enabling quick book issuing, reissuing and searching [36]. Below are the advantages of RFID system:…”

Section: Using the Rfid Technology System In Librariesmentioning

confidence: 99%

A Systematic Review on IoT-Based Smart Technologies for Seat Occupancy and Reservation Needs in Smart Libraries at Institution of Higher Learning

Reneilwe Maepa,

Nthabiseng Moeti

2024

Internet of Things - New Insights

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The introduction of industry 4.0 technologies, including artificial intelligence (AI), the Internet of Things (IoT), and other cutting-edge technological developments, has completely transformed traditional library practises in higher education. Despite the topic’s unquestionable importance, the main objective of this chapter is to address the effects of IoT technology and it is inconsistent and dispersed. However, there are various challenges, such as accurate reservation methods, real-time seat occupancy tracking, and reservation time estimation. To discover, compare, and characterize current investigations in the Smart Library Seat Occupancy and Reservation system (SLSORS), this proposed book chapter examined articles published between 2016 and 2022. In the SLSORS, we will also give a thorough taxonomy and perform a technical analysis of the articles. This provides the much-needed clarity regarding the problems associated with SLSORS and their available literature-based solutions. The fundamental taxonomy is framed by the reservation security, seat reservation, seat selection, and seat availability criteria. Thus, the benefits and drawbacks of the selected approaches are also offered, along with a full comparison of evaluation methodology, evaluation tools, and evaluation metrics. Furthermore, this chapter incorporates all processes and method for SLSORS and drew attention to the ongoing challenges that this chapter is seeking to address.

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Section: Using the Rfid Technology System In Librariesmentioning

confidence: 99%

A Systematic Review on IoT-Based Smart Technologies for Seat Occupancy and Reservation Needs in Smart Libraries at Institution of Higher Learning

Reneilwe Maepa,

Nthabiseng Moeti

2024

Internet of Things - New Insights

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“…Different variations of the autoregressive integrated moving average (ARIMA) model have been used in commodity price analysis. Both simple and seasonal ARIMA models (SARIMA) have been applied in analyzing price behavior of nonperishable commodities in South Asia (Bogahawatta,1988;Ansari & Ahmad, 2001;Hossain, Samad, & Ali, 2006;Kamu, Ahamed, & Yusoff, 2008;Jadhav, Reddy, & Gaddi, 2017). Fewer studies have been conducted on price fluctuations of perishables, compared to nonperishable agricultural commodities.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Price Behavior in Sri Lankan Vegetable Market

Champika¹,

Mugera²

2023

JABM

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Vegetables are important source of nutrient for the Sri Lankan population and both farmers and consumers are adversely affected by vegetable price volatility. The lack of price analysis and forecasting has made it difficult to establish an effective early warning system for the vegetable farming sector in Sri Lanka. This study investigates the price behaviour of selected fresh vegetables - carrot, cabbage, and tomato - and forecasts the future prices and volatilities using time series techniques. Analysis of weekly price data from 1997 to 2018 revealed that all three - price series had one structural break, but none coincided with the policy change when the government introduced fertilizer subsidies for vegetable producers in the agriculture sector. The autoregressive integrated moving average (ARIMA) model estimations show that the best model for forecasting carrot price is ARIMA (3,1,2) (0,0,2)[52]* capable of predicting retail prices at 71% accuracy while the best model for cabbage prices is ARIMA(1,1,1)(0,0,1)[52] with a prediction accuracy of 55%. All three-price series exhibit serial correlation in residuals; hence GARCH estimations were used to model and predict volatility. Of the fitted ARMA GARCH models, the best model for estimating the volatility of carrot and cabbage were GARCH (1, 2) ARMA (3, 2) and GARCH (1,1) ARMA (3 ,2), respectively. The volatility predictions for the first ten weeks for the year 2019 indicate a gradual decrease in volatility in the carrot price series whilst a gradual increase in volatility in the cabbage price series.

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“…They can forecast future trends based on existing data. Jadhav et al. (2017) used the univariate ARIMA model to predict paddy, ragi and maize prices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They can forecast future trends based on existing data. Jadhav et al (2017) used the univariate ARIMA model to predict paddy, ragi and maize prices. To improve the prediction accuracy and select better models, many researchers combined ARIMA with machine learning techniques and achieved superior results (Dhini et al, 2015;Kraemer et al, 2020;Shahriar et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Food price dynamics and regional clusters: machine learning analysis of egg prices in China

Chang

Zhou

Höschle

et al. 2022

CAER

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PurposeThe study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.Design/methodology/approachThe study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.FindingsThe study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.Practical implicationsThe machine learning techniques could help governments make more precise policies and help producers make better investment decisions.Originality/valueThis is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

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Smart Agricultural System

Cited by 4 publications

References 0 publications

A Systematic Review on IoT-Based Smart Technologies for Seat Occupancy and Reservation Needs in Smart Libraries at Institution of Higher Learning

A Systematic Review on IoT-Based Smart Technologies for Seat Occupancy and Reservation Needs in Smart Libraries at Institution of Higher Learning

Analysis of Price Behavior in Sri Lankan Vegetable Market

Food price dynamics and regional clusters: machine learning analysis of egg prices in China

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