Predictive analytics using cross media features in precision farming

Kolipaka, Venkata Rama Rao

doi:10.1007/s10772-020-09669-z

Cited by 11 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thereby, it is necessary to improve the accuracy of resource search, fully use the big data-driven artificial intelligence technology, and carry out in-depth research on the intelligent and accurate search of cross-media big data. It has important theoretical significance and wide application value [ 19 , 20 ]. Figure 3 shows the acquisition process of cross-media education big data.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network

Cheng

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This exploration aims to study the value orientation and essence of early childhood enlightenment education based on the deep neural network (DNN). Based on the acquisition and feature learning of cross-media education big data, the DNN correlation learning of cross-media education big data, and the intelligent search of cross-media education big data based on the DNN, the intelligent search system of cross-media children’s enlightenment education big data based on DNN is designed and implemented. The system includes three functional modules: the feature learning module of cross-media infant enlightenment education big data, the deep semantic correlation learning module of cross-media childhood enlightenment education big data, and the intelligent search module of cross-media childhood enlightenment education big data based on DNN. This exploration realizes the acquisition and feature learning of big data of cross-media early childhood enlightenment education, DNN learning of cross-media education big data of early childhood enlightenment, and intelligent computing of cross-media education big data based on DNN. The experimental results show that the proposed system’s mean average precision (MAP) performance is improved by 15.6% on the public dataset of early childhood enlightenment education published by the Ministry of Education. Moreover, the system has also significantly improved the MAP performance of the constructed dataset in the field of early childhood enlightenment education; that is, the MAP performance has been improved by 20.6% on the dataset of Taylor’s University in Malaysia (NUS-WIDE). This exploration has certain theoretical significance and empirical value for early childhood enlightenment education research.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Computational Intelligence and Neuroscience search of cross-media big data. It has important theoretical significance and wide application value [19,20]. Figure 3 shows the acquisition process of cross-media education big data.…”

Section: Feature Learning and Network Correlation Learning Of Cross-m...mentioning

confidence: 99%

Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network

Cheng

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Literature Review 2022, Editorial Universitat Politècnica de València technologies of Precision Agriculture (PA) (Kolipaka, 2020;Lioutas et al, 2019): Data acquisition, Data analysis, and evaluation, and Precision application.…”

Section: Opportunities and Barriers Of Smart Farming Adoption By Farm...mentioning

confidence: 99%

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Sarmiento

Sánchez

Lamos-Díaz

2023

Proceedings INNODOCT/22. International Conference on Innovation, Documentation and Education

View full text Add to dashboard Cite

Smart Farming is a revolutionary paradigm in the agri-food sector that integrates real-time data collection through various sensors and sources (i.e., the Internet of Things technologies (IoT) such as automation systems, farm bots, drones, and technological computer infrastructure). These integrated solutions support more intelligent decisions in the agricultural sector, increasing competitiveness and productivity in rural areas. However, there are difficulties with interoperability, security, data governance, farming practices diversity, farmer capacitation, and technology diffusion. End-users are heterogeneous, from illiterate producers to farm enterprises, which involves a custom ICT adoption strategy for each potential customer. This paper presents a systematic literature review that identifies the opportunities and barriers to adopting Smart Farming solutions in rural areas, highlighting the need to implement centered-user design strategies to increase the technology adoption considering two different types of farmers.

show abstract

“…An architecture named Agri-info was developed to provide user services. Venkata Rama Rao Kolipaka [86], proposed a predictive analysis using cross media references for precision farming. Authors focused on the usage of sensors and MEMS integrated IoT for precision farming.…”

Section: A Iot In Farm Managementmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

View full text Add to dashboard Cite

A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

show abstract

Predictive analytics using cross media features in precision farming

Cited by 11 publications

References 40 publications

Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network

Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Recent Developments of the Internet of Things in Agriculture: A Survey

Contact Info

Product

Resources

About