Design of a Comprehensive Sensor based Soil and Crop Analysis System Model using Machine Learning Algorithms

H, Aafreen Sana; Prathibha, Soma; P, Pravin Kumar; M, Shabika Fathima; B, Yashwanth Krishnan

doi:10.1109/rdcape52977.2021.9633648

2021 4th International Conference on Recent Developments in Control, Automation &Amp; Power Engineering (RDCAPE) 2021

DOI: 10.1109/rdcape52977.2021.9633648

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Design of a Comprehensive Sensor based Soil and Crop Analysis System Model using Machine Learning Algorithms

Aafreen Sana H¹,

Soma Prathibha²,

Pravin Kumar P³

et al.

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Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

Sharma

Sharma²,

Sharma³

et al. 2022

Journal of Sensors

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Agriculture has become more industrialized and intensive due to the rising demand for food in quality and quantity. Agricultural modernization will be made possible by the Internet of Things (IoT), a technology with a great promise for revolutionizing the industry. Agricultural products will be in high demand by 2050 due to a 30% increase in the global population, so there is a need to devise new mechanisms for agriculture, and smart agriculture is one of those mechanisms; however, smart agriculture needs to be explored further to realize its potential fully. So, to explore the potential of this field, the researchers have used a corpus that is extracted from the Scopus database from the year 2008 to the year 2022 and applied the LDA technique. A corpus of 4309 articles was selected from the Scopus database to apply the latent Dirichlet analysis (LDA) model to predict research areas for smart agriculture. Using IoT technology, farmers and producers may better manage their resources, such as fertilizer consumption and the number of trips made by farm vehicles, while minimizing waste and maximizing productivity, including water, electricity, and other inputs. This data-driven experimental study identifies smart agriculture research trends by implementing a topic modeling technique previously used in smart agriculture. The authors have created seventeen research themes in smart agriculture based on the LDA topic modeling. This analysis suggests that the indicated areas are in the growth phase and require further research and exploration.

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Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

Sharma

Sharma²,

Sharma³

et al. 2022

Journal of Sensors

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show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Design of a Comprehensive Sensor based Soil and Crop Analysis System Model using Machine Learning Algorithms

Cited by 1 publication

References 12 publications

Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

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