Abstract

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Highlights

  • Big Data technology combines the mathematical models in the smart agriculture domain to seamlessly analyse a large amount of data in agricultural production and provide valuable insights to the farmer without the need of the dedicated specialist

  • Two main approaches have been widely applied in agricultural data acquisition: a) Site survey with dedicated professionals [2]; b) Sensing technology based on Space-Air-Ground Integrated Network (SAGIN) [3]

  • Comparing with existing agricultural data collection method, we further analyze the advantages and disadvantages of applying AMCS in the farming scenario. (i) We analyze the crucial factors of combining MCS with agriculture, including the number of potential users, developed agriculture-related APPs, farmer’s experience, and cooperation between agribusiness and farmers; (iii) We propose six application scenarios and list future research issues of AMCS

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Summary

INTRODUCTION

Big Data technology combines the mathematical models in the smart agriculture domain to seamlessly analyse a large amount of data in agricultural production and provide valuable insights to the farmer without the need of the dedicated specialist. As a result of our extensive evaluation, we find that MCS technology has been widely applied in various scenarios, e.g., smart environment, smart service, smart transportation, smart health, and smart social, leading to several research directions including function realization, participant selection, task allocation, incentive strategies, data mining and visualization, and privacy protection. In these researches, citizens are the major participant of sensing task for MCS.

DATA COLLECTION IN SMART AGRICULTURE
Data varieties in farming
Existing data collection method
Comparison
2) Summary
THE CRUCIAL FACTOR OF COMBINING AMCS WITH
Developed agriculture-related APPs
Farmer’s experience
Lots of potential users
Cooperation between agribusiness and farmers
Measure cultivated area
Collect meteorological disaster information
Collect pest and disease images
Plan for production
Cooperative sensing with IAM
Identify the quality of fruits
OPEN RESEARCH ISSUES BASED ON AGRICULTURAL CHARACTERISTICS
Agricultural Characteristics
General Research Issues
Findings
CONCLUSION AND INSIGHT
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