On enabling mobile crowd sensing for data collection in smart agriculture: a vision

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

SI values in the three groups (mean [SD]).

<p>LDL-C: low-density lipoprotein cholesterol, SI: smoothness index, TC: total cholesterol.</p><p>*: <i>P</i><0.01,</p>c<p>: compared with group 1,</p>d<p>: compared with group 2.</p

The immuohistochemical staining of atherosclerosis in the 3 groups.

<p>Legend: Magnifications: ×400. MMP-9: matrix metalloproteinase-9; LOX-1: lectin-like oxidized low density lipoprotein receptor-1. Representative pictures of MMP-9 and LOX-1 expressions immuohistochemical staining in the aortic atherosclerotic plaque of the three groups.</p

The IMT of the three groups in different weeks (mean [SD], mm).

<p>IMT: intima-media thickness.</p>#<p>: <i>P</i><0.05,</p><p>*: <i>P</i><0.01,</p>a<p>: compared with 0 week,</p>b<p>: compared with week 12;</p>c<p>: compared with group 1,</p>d<p>: compared with group 2.</p

The fluctuations of serum lipids level in the three groups.

<p>The fluctuations of serum lipids level in the three groups.</p

The vascular ultrasound of group 2 and group 3 in different weeks.

<p>Legend: (A) and (C): group 2 at week 12 and week 24; (B) and (D): group 3 at week 12 and week 24.</p

Body weight in the three groups (mean [SD]).

#<p>: <i>P</i><0.05,</p><p>*: <i>P</i><0.01,</p>a<p>: compared with 0 week,</p>b<p>: compared with week 12.</p

The characteristics of atherosclerotic plaque in the three groups (mean [SD]).

#<p>: <i>P</i><0.05,</p><p>*: <i>P</i><0.01,</p>c<p>: compared with group 1,</p>d<p>: compared with group 2.</p

Serum hs-CRP, IL-18, ET-1, OX-LDL and NO levels in the three groups (mean [SD]).

<p>ET-1: endothelin-1, hs-CRP: high-sensitivity C-reactive protein, IL-18: interleukin-18, NO: nitric oxide, OX-LDL: oxidized low density lipoprotein.</p>#<p>: <i>P</i><0.05,</p><p>*: <i>P</i><0.01,</p>a<p>: compared with 0 week,</p>b<p>: compared with week 12;</p>c<p>: compared with group 1,</p>d<p>:compared with group 2.</p

The H&E staining of atherosclerosis in the three groups.

<p>Legend: Magnifications: ×200(A–C2), ×400(C3). A: Normal aorta intima in group 1. B1–B2: Atherosclerotic plaque in group 2. The endothelial cells shed, numerous foam cells and cholesterol crystal (B1); cell necrosis and calcium deposition (B2). C1–C3: Atherosclerotic plaque in group 3. plaques with thin fibrous caps and big lipid cores (C1), the discontinuous fiber cap (C2), inflammatory cells (C3).</p