Search results for "precision agriculture"

showing 10 items of 34 documents

Analysis of Biophysical Variables in an Onion Crop (Allium cepa L.) with Nitrogen Fertilization by Sentinel-2 Observations

2022

The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. Ac cording to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year−1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with …

NitrogenNitrógenoLeaf Area IndexPrecision AgricultureIndice de Superfície FoliarIndice de VegetaciónCebollaRemote SensingAgricultura de PrecisiónOnionsSentinel - 2Teledetecciónvegetation index; LAI; nitrogen; remote sensing; Sentinel-2; precision farmingCentinela -2Agronomy and Crop ScienceVegetation IndexAgronomy; Volume 12; Issue 8; Pages: 1884
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A crop field modeling to simulate agronomic images

2010

In precision agriculture, crop/weed discrimination is often based on image analysis but though several algorithms using spatial information have been proposed, not any has been tested on relevant databases. A simple model that simulates virtual fields is developed to evaluate these algorithms. Virtual fields are made of crops, arranged according to agricultural practices and represented by simple patterns, and weeds that are spatially distributed using a statistical approach. Then, experimental devices using cameras are simulated with a pinhole model. Its ability to characterize the spatial reality is demonstrated through different pairs (real, virtual) of pictures. Two spatial descriptors …

PixelComputer sciencebusiness.industryNearest neighbour algorithmComputer visionImage processingPrecision agricultureArtificial intelligenceFunction (mathematics)Virtual realityReal imagebusinessSpatial analysis2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP)
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A system for the real-time geo-referenced measurement of soil parameters

2011

The aim of this research is to develop a system for accurately measuring in real-time, collecting and processing a high amount of geo-referenced data of soil physical-mechanical parameters, e.g. cone penetrometer resistance, index of soil compaction, and draft force. The system for measuring the soil cone penetrometer resistance is comprised of a load cell, connected to a rod, ending with a cone, and is mounted on a frame, fixed to the front part of a tractor. The system for measuring the draft force required to till the soil is comprised of a load cell, mounted on the hitch hook of a tool carrier, towed by the tractor. Moreover, in order to test the usefulness of the system with different …

Precision agricultureAgricultural and Biological Sciences (all)penetrometer soil compaction spatial variability precision agricultureSettore AGR/09 - Meccanica AgrariaVeterinary (all)Penetrometer; Precision agriculture; Soil compaction; Spatial variability; Agricultural and Biological Sciences (all); Veterinary (all)PenetrometerSoil compactionSpatial variability
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Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

2022

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative result…

Precision agriculturemultispectralbiotic and abiotic stresatelliteSoil Sciencesolar induced fluorescenceGeologymulti-modalPrecision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stressUNESCO::CIENCIAS TECNOLÓGICASITC-HYBRIDhyperspectralITC-ISI-JOURNAL-ARTICLEddc:550Computers in Earth Sciences
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A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives

2023

This paper provides a survey on the adoption of LoRa in the agricultural field, and reviews state-of-the-art solutions for Smart Agriculture, analyzing the potential of this technology in different infield applications. In particular, we consider four reference scenarios, namely irrigation systems, plantation and crop monitoring, tree monitoring, and livestock monitoring, which exhibit heterogeneous requirements in terms of network bandwidth, density, sensors’ complexity, and energy demand, as well as latency in the decision process. We discuss how LoRa-based solutions can work in these scenarios, analyzing their scalability, interoperability, network architecture, and energy-efficiency. Fi…

Settore ING-INF/03 - TelecomunicazioniComputer Networks and CommunicationsHardware and ArchitectureSignal ProcessingInternet of Things Internet of Things (IoT) LoRa LoRaWAN LPWAN Monitoring Power demand Precision Agriculture Sensors Smart Agriculture Smart farming Temperature sensors Wireless sensor networks Wireless Sensor Networks (WSN)Settore ING-INF/01 - ElettronicaComputer Science ApplicationsInformation SystemsIEEE Internet of Things Journal
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A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems

2013

Abstract Leaf area index (LAI) is a key biophysical parameter for the monitoring of agroecosystems. Conventional two-band vegetation indices based on red and near-infrared relationships such as the normalized difference vegetation index (NDVI) are well known to suffer from saturation at moderate-to-high LAI values (3–5). To bypass this saturation effect, in this work a robust alternative has been proposed for the estimation of green LAI over a wide variety of crop types. By using data from European Space Agency (ESA) campaigns SPARC 2003 and 2004 (Barrax, Spain) experimental LAI values over 9 different crop types have been collected while at the same time spaceborne imagery have been acquir…

Spectral indexSoil ScienceRed edgeHyperspectral imagingSatellitePlant SciencePrecision agricultureVegetationLeaf area indexAgronomy and Crop ScienceNormalized Difference Vegetation IndexMathematicsRemote sensingEuropean Journal of Agronomy
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A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

2022

Abstract In precision agriculture, the accurate segmentation of crops and weeds in agronomic images has always been the center of attention. Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds. This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images. Agronomic images of two different databases were used for the segmentation algorithms. Using the thresholding technique, everything except plants was removed from the images. Afterward, semantic segmentation was applied usin…

Subtractive colorComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONConfusion matrixForestryAquatic ScienceThresholdingAccurate segmentationComputer Science ApplicationsClassification rateAnimal Science and ZoologySegmentationPrecision agricultureCluster analysisAgronomy and Crop ScienceAlgorithmInformation Processing in Agriculture
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Long-range & Self-powered IoT Devices for Agriculture & Aquaponics Based on Multi-hop Topology

2019

This article presents the prototype design and testing of a long-range, self-powered IoT devices for use in precision agriculture and aquaponics. The devices are designed using the ultra-low power nRF52840 microcontroller with Bluetooth 5 support and ambient energy harvesting. A power of 942µW is harvested in an indoor environment. The devices are therefore suitable for both indoor and outdoor use, as natural sunlight will provide far more energy compared to artificial indoor lights. A line-of-sight range of up to 1.8km is achieved with the use of coded transmissions. However, the coverage area and range can be extended significantly by deploying the devices in multi-hop network topology. T…

Sunlight021110 strategic defence & security studiesComputer sciencebusiness.industryReal-time computing0211 other engineering and technologies020206 networking & telecommunicationsCloud computing02 engineering and technologyNetwork topologylaw.inventionBluetoothlawAgriculture0202 electrical engineering electronic engineering information engineeringPrecision agriculturebusinessEnergy harvestingWireless sensor networkEfficient energy use2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
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A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm histo…

2013

Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this…

UAVtaskField (computer science)wheat/dk/atira/pure/sustainabledevelopmentgoals/zero_hungerfarm machinerySDG 2 - Zero HungerVariable Rate Applicationta119Remote sensingData collectionAgricultural machinerybusiness.industryprecision farmingHyperspectral imagingcomputer.file_formatta4111fertilizerhyperspectralGeographyVRAPrecision agricultureRaster graphicsScale (map)businesscomputer
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Roughness evaluation of vine leaf by image processing

2013

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…

[ MATH ] Mathematics [math]0106 biological sciences0209 industrial biotechnologyScanning electron microscope[SDV]Life Sciences [q-bio]Computer Vision[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[MATH] Mathematics [math]02 engineering and technologySurface finishLeaf roughness01 natural sciences[PHYS] Physics [physics][SPI]Engineering Sciences [physics]020901 industrial engineering & automation[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SPI ] Engineering Sciences [physics]Surface roughnessComputer vision[MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[PHYS]Physics [physics][ PHYS ] Physics [physics]Artificial neural network[STAT]Statistics [stat]Multilayer perceptron[SDE]Environmental SciencesBiological system[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMaterials science[ STAT ] Statistics [stat][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics]IASTEDFast Fourier transformNeural NetworkImage processingImage processing[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyTexturelanguage technologies[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPrecision agriculturebusiness.industry[STAT] Statistics [stat]Precision agricultureArtificial intelligencebusiness010606 plant biology & botany
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