Satellite Network Design Approach to Orbit Determination for GNSS

Mishra, Shivam; Sharma, Vishali

doi:10.17485/ijst/2016/v9i47/106449

Cited by 45 publications

(2 citation statements)

References 6 publications

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“…Bayesian Network reflects the conditional dependencies of a set of variables and represents them using a graphical and probabilistic model. Markov Chain Model is a probabilistic mathematical model which finds output based on previous experience [140].…”

Section: Crop Planningmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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Section: Crop Planningmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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“…Machine learning (ML) approaches are used for crop prediction using several mathematical and statistical methods, namely artificial neural networks, fuzzy information networks, decision tree, regression analysis, clustering, principal component analysis, Bayesian belief network, time series analysis, and Markov chain model. The application of these machine learning techniques in crop cultivation shows more tremendous advantages due to the availability of many data from several resources to obtain hidden knowledge [9]. Considering the need for machine learning techniques, a wide range of literature surveys is essential to derive a novel proposition to predict the crop yield's accuracy further.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

2021

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

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A Study on Various Machine Learning Algorithms and Their Role in Agriculture

Rangra

Choudhury

2021

Agricultural Informatics

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Satellite Network Design Approach to Orbit Determination for GNSS

Cited by 45 publications

References 6 publications

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

A Study on Various Machine Learning Algorithms and Their Role in Agriculture

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