Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture

Cambra, Carlos; Díaz, J.R.; Lloret, Jaime

doi:10.1007/978-3-662-46338-3_14

Cited by 30 publications

(17 citation statements)

References 14 publications

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“…Our system has the potential to provide accurate information for use in many novel applications. For example, our system can distinguish weeds from crops based on the smoothness of the leaves and the structure of their skeletons, thereby providing information about the locations of weeds to facilitate precision spraying and improve efficiency (Cambra et al, 2015). Our system can also be used to analyze individual leaves to combat insect pests and crop diseases.…”

Section: Discussionmentioning

confidence: 99%

Individual leaf identification from horticultural crop images based on the leaf skeleton

Zhang

Weckler

Wang

et al. 2016

Computers and Electronics in Agriculture

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Section: Discussionmentioning

confidence: 99%

Individual leaf identification from horticultural crop images based on the leaf skeleton

Zhang

Weckler

Wang

et al. 2016

Computers and Electronics in Agriculture

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“…Candiago et al [21] uses a drone equipped with a Tetracam ADC Micro camera for acquiring images in the red (R), green (G) and near-infrared (NIR) bands, allowing to calculate some of the most common vegetation indices, i.e., the normalized difference vegetation index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI) and the Soil Adjusted Vegetation Index (SAVI). With their work, they demonstrate the great potential of drones in precision agriculture Another example of the use of drones with cameras is the proposal presented by Cambra et al [22]. Authors presented a network (with an ad hoc communication protocol) consisting of a drone, several sensors and pressure sprayer.…”

Section: Related Workmentioning

confidence: 95%

Autonomous WSN for Lawns Monitoring in Smart Cities

Marín

Rocher

Parra

et al. 2017

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)

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The urban lawns are frequently composed by different grass species to combat some problems of water scarcity and diseases. In order to maintain these lawns, high amount of water is required. Nowadays, smart cities can be understood as a new concept of city that includes, among others, efficient distribution of energy, water, and other resources by using technology. In these cases, the main challenge is to try to estimate the necessary amount of water for irrigation and the phytosanitary uses without wasting water. In this paper, we propose a method to identify the percentage of grass coverage in lawns to deduce the grass productivity and estimate the most accurate quantity of water to ensure a good production of grass. The system is based on a Smart Autonomous Vehicle (SAV) controlled by an Arduino Mega 2560. It also contains an array of 120 colour sensors used to gather the data. The selected colour sensor is a TCS3472. With these sensors, we obtain the RGB histograms of the lawns. For these experiments, we have several lawn parcels of 1.5 x 1 m. From these, a matrix of 150 x 100 RGB values is obtained. After processing the green values of matrix, we have observed a correlation between the level of coverage and these values. The grass coverage is related with values of brightness between 40 and 60 which allow us to classify the lawn as a function of its coverage and the irrigation needs.

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“…Therefore, this kind of performance is tracked and recognized for further data process, this kind of example is given in [5], where the highly multi-spectral cameras is used to display the vegetarian directory through the band reflection process. In this paper [6], it presented the analysis to regulate requirement of bandwidth to transmit the video in system and can be applied to investigate the problem in an irrigation coefficient model. They also investigated weeds on maize crop and the main aim of their effort was to increase the monitoring process with sensors 'or' adding all the sensor networks, where the airborne sensors functionality is just a type of sensors.…”

Section: Literature Surveymentioning

confidence: 99%

Cloud Computing Model for Agricultural Applications

Vimala¹,

Ranjan²

2019

IJRTE

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There are major advancements in the technological fields, which include the smart farming with the IoT, Big-data, Cloud-service oriented architectures along with the internet etc. While considering the current situation in the field of agriculture, there are many issues being faced. Some of the major problems are climatic-changes, lack of the fresh-water availability, much barren lands and decrease in the farming lands, less power availability, etc. The agricultural architecture that are cloud-based or the cloud-service oriented architecture includes the monitoring of farming and the warehousing-information. In this paper, we provide a work on IoT, aerial images and SOA and how they can be applied on the large and the smart farming systems, which is scalable and that can be easily configured. An event-driven SOA is necessary to facilitate the flawless and significant information and integration of distributed and heterogeneous internet agricultural services to help real-time user preferences.

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Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture

Cited by 30 publications

References 14 publications

Individual leaf identification from horticultural crop images based on the leaf skeleton

Individual leaf identification from horticultural crop images based on the leaf skeleton

Autonomous WSN for Lawns Monitoring in Smart Cities

Cloud Computing Model for Agricultural Applications

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