Urban Lawn Monitoring in Smart City Environments

Marín, José; Parra, Lorena; Rocher, Javier; Sendra, Sandra; Lloret, Jaime; Mauri, Pedro V.; Masaguer, Alberto

doi:10.1155/2018/8743179

Cited by 22 publications

(20 citation statements)

References 19 publications

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“…In addition, the use of information and communication technology offers the possibility of monitoring the grass state in order to adjust the irrigation regime [33,34].…”

Section: Potential Of Spectral Vegetation Indices To Evaluate Turfgramentioning

confidence: 99%

RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions

Marín

Yousfi²,

Mauri³

et al. 2020

Sustainability

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Grasslands have a natural capacity to decrease air pollution and a positive impact on human life. However, their maintenance requires adequate irrigation, which is difficult to apply in many regions where drought and high temperatures are frequent. Therefore, the selection of grass species more tolerant to a lack of irrigation is a fundamental criterion for green space planification. This study compared responses to deficit irrigation of different turfgrass mixtures: a C4 turfgrass mixture, Cynodon dactylon-Brachypodium distachyon (A), a C4 turfgrass mixture, Buchloe dactyloides-Brachypodium distachyon (B), and a standard C3 mixture formed by Lolium perenne-Festuca arundinacea-Poa pratensis (C). Three different irrigation regimes were assayed, full irrigated to 100% (FI-100), deficit irrigated to 75% (DI-75), and deficit irrigated to 50% (DI-50) of container capacity. Biomass, normalized difference vegetation index (NDVI), green area (GA), and greener area (GGA) vegetation indices were measured. Irrigation significantly affected the NDVI, biomass, GA, and GGA. The most severe condition in terms of decreasing biomass and vegetation indices was DI-50. Both mixtures (A) and (B) exhibited higher biomass, NDVI, GA, and GGA than the standard under deficit irrigation. This study highlights the superiority of (A) mixture under deficit irrigation, which showed similar values of biomass and vegetation indices under full irrigated and deficit irrigated (DI-75) container capacities.

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“…In addition, the use of information and communication technology offers the possibility of monitoring the grass state in order to adjust the irrigation regime [33,34].…”

Section: Potential Of Spectral Vegetation Indices To Evaluate Turfgramentioning

confidence: 99%

RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions

Marín

Yousfi²,

Mauri³

et al. 2020

Sustainability

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“…Examples of VIs using multispectral images are listed in Table 4, and each index has its own advantages and disadvantages. The Normalized Difference Vegetation Index (NDVI) is a well-known VI which has values ranging from −1.0 to 1.0, negative values represent water, values −0.1 to 0.1 correspond to soil, and positive values indicate target plants due to their greenness [16,22,27]. However, the NDVI is limited by errors due to atmospheric influence and soil reflectance [80].…”

Section: Spectral Sensorsmentioning

confidence: 99%

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Jang

Kim

et al. 2020

Remote Sensing

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Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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“…The vegetation can also be in urban environments. For that reason, José Marín et al presented in [23] a remote sensing system for urban lawn monitoring in smart cities. The system is comprised of an Arduino node with a camera mounted on a drone.…”

Section: Related Workmentioning

confidence: 99%

DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

et al. 2020

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The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m2 to 1 node each 5000 m2) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity.

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Urban Lawn Monitoring in Smart City Environments

Cited by 22 publications

References 19 publications

RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions

RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

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