A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues

Ruan, Junhu; Wang, Yuxuan; Chan, Felix T. S.; Hu, Xiangpei; Zhao, Minjuan; Zhu, Fei; Shi, Baofeng; Shi, Yan; Lin, Fan

doi:10.1109/mcom.2019.1800332

Cited by 148 publications

(56 citation statements)

References 9 publications

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“…A life cycle framework of green IoT-based agriculture [12] -Recognizes the quality of agriculture ingredients.…”

Section: Model Advantages Disadvantagesmentioning

confidence: 99%

“…Water management and optimal water supply have become popular topics in the field of smart agriculture and are being studied and developed by many researchers. Over the years, prominent researchers in the field of smart agriculture have written many survey articles which introduce the research methodology for various communication technologies that control resources efficiently based on IoT technology [11][12][13]. We summarize and compare our model with the existing literature in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Khoa

Man

Nguyen

et al. 2019

JSAN

100

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Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign.

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“…A life cycle framework of green IoT-based agriculture [12] -Recognizes the quality of agriculture ingredients.…”

Section: Model Advantages Disadvantagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Khoa

Man

Nguyen

et al. 2019

JSAN

100

View full text Add to dashboard Cite

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“…Some future studies are possible. To encourage the unsatisfied customers to accept the backorder invitation, some newsvendors provide price discount on retail price for the backlogged items [38][39][40]. Thus, a possible extension on this study would be introducing price discount on retail price into this research.…”

Section: Discussionmentioning

confidence: 99%

Hedging Risks in the Loss-Averse Newsvendor Problem with Backlogging

Liu

Chan

2019

Mathematics

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This paper studies the optimal order decisions for the loss-averse newsvendor problem with backordering and contributes to the risk hedging issue in the newsvendor model. The Conditional Value-at-Risk (CVaR) measure is applied to quantify the potential risks for the loss-averse newsvendor in a backordering setting, and we obtain the optimal order quantity for a loss-averse newsvendor to maximize the CVaR of utility. It is found that the optimal order quantity to maximize the CVaR objective could be bigger or smaller than the expected profit maximization (EPM) order quantity, which provides an alternative explanation on decision bias in the newsvendor model. This study also reveals that the optimal order quantity for a loss-averse newsvendor to maximize expected utility with backordering is smaller than the EPM order quantity, which implies that backordering encourages the loss-averse newsvendor to order fewer items. Sensitivity analyses are performed to investigate the properties of the optimal order quantities and managerial insights are suggested. This paper provides a novel method for the risk management of the loss-averse newsvendor model and presents several new ordering policies for the retailers in practice.

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“…In [24], the authors provide optimal management solutions to efficiently identify nutrients and water; a multi-objective genetic algorithm was used to implement an E-Water system. Finally, in [19], [20] and [21] the authors presented interesting decision support systems for early warning, soil nutrient and financial services, respectively. However, all the mentioned works did not tackle agricultural Big Data integration.…”

Section: Related Workmentioning

confidence: 99%

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Ngo

Kechadi

2020

Proceedings of the 4th International Conference on Machine Learning and Soft Computing

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Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.

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A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues

Cited by 148 publications

References 9 publications

Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Hedging Risks in the Loss-Averse Newsvendor Problem with Backlogging

Crop Knowledge Discovery Based on Agricultural Big Data Integration

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