Monitoring of crop fields using multispectral and thermal imagery from UAV

Raeva, P.; Šedina, Jaroslav; Dlesk, A.

doi:10.1080/22797254.2018.1527661

Cited by 99 publications

(62 citation statements)

References 4 publications

(3 reference statements)

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“…Pix4D [29,35,55] The most commonly used tool. It can be used for calculating VIs and/or constructing of 3D models and orthomosaics…”

Section: Software Tool Descriptionmentioning

confidence: 99%

“…Vegetation Indices (VIs) have been widely used in remote sensing applications for Precision Agriculture. They are considered to be very effective for monitoring the growth and health of crops in qualitative and quantitative vegetation analysis [25,29,30]. Vegetation Indices are based on the absorption of electromagnetic radiation by the vegetation.…”

Section: Vegetation Indicesmentioning

confidence: 99%

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A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…Pix4D [29,35,55] The most commonly used tool. It can be used for calculating VIs and/or constructing of 3D models and orthomosaics…”

Section: Software Tool Descriptionmentioning

confidence: 99%

Section: Vegetation Indicesmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…De fato, em diversas culturas, as bandas localizadas no intervalo do termal e os índices de vegetação contribuem para detecção de parâmetros e atributos agronômicos relacionados à presença de doenças (Martins et al, 2017), porém deve-se ressaltar que a alta resolução espacial de bandas centradas nos intervalos do visível também contribuir no monitoramento de parâmetros físicos (IAF) de culturas com folhas grandes como a do milho (Raeva et al, 2018).…”

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Uso de imagens tomadas por aronaves remotamente pilotadas para detecção da cultura do milho infestadas por Spodoptera frugiperda

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Abreu

Martins

et al. 2020

Rev. Bras. Geog. Fis.

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A Spodoptera frugiperda (Smith) (lagarta-do-cartucho) é a principal praga do milho, com a intensificação da agricultura, os cultivos sucessivos possibilitam maior infestação pela praga. Isso levou ao surgimento de populações resistentes a inseticidas e culturas transgênicas. Pesquisas de campo para iniciar tratamentos com inseticidas são demoradas e exaustivas. Pensando em agilidade e qualidade, neste trabalho foi utilizado uma aeronave remotamente pilotada (ARP) equipado com uma câmera RGB e uma câmera MAPPIR 3 para capturar imagens de uma lavoura de milho, com o objetivo de estimar o índice de área foliar (IAF) de um talhão infestado por S. frugiperda. Durante o ciclo da cultura do milho foram realizadas várias avaliações: determinação do índice de área foliar (IAF), severidade do ataque da praga e, voos para aquisição das imagens. Modelos radiométricos para estimativa do IAF foram obtidos a partir de modelos de regressão linear compostos pelas bandas que melhor correlacionaram com os parâmetros medidos. Os resultados obtidos demonstraram eficiência e maior precisão na estimativa do IAF para o modelo radiométrico composto pela a banda do infravermelho próximo na câmera MAPPIR 3. Nesta ocasião, o RMSE calculado foi de 885,0714 cm². Use of images for attack detection of Spodoptera frugiperda in corn under function of loss of foliar area A B S T R A C TSpodoptera frugiperda (Smith) (cartridge-caterpillar) is the main pest of maize, with the intensification of agriculture, the successive crops allow greater infestation by the pest. This has led to the emergence of populations resistant to insecticides and transgenic crops. Field research to begin treatments with insecticides is time consuming and exhaustive. Thus, thinking of agility and quality, in this work was used a remotely piloted aircraft (ARP) equipped with an RGB camera and a MAPPIR 3 camera to capture images of a maize crop with the objective of estimating the leaf area index (LAI) of a field infested by S. frugiperda. The radiometric models for the estimation of LAI were obtained from linear regression models composed by the bands that best correlated with the measured parameters. The obtained results demonstrated efficiency and greater accuracy in the estimation of the LAI for the radiometric model composed by the near infrared band (IVP) in the MAPPIR 3 camera. On this occasion, the RMSE calculated was 885.0714 cm².Keywords: Corn; Cartridge Caterpillar; Remotely Piloted Aircraft.

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“…Malbéteau et al [27] estimated emissivity in terms of vegetation cover, expressed by vegetation index values [17,19]. Some studies, however, do not take emissivity into account and therefore provide results that should be viewed as qualitative rather than quantitative [14,28]. To overcome the challenges mentioned, we have introduced a dual sensor system that was combined with a thermal imager that had a much higher sensor resolution than those available off-the-shelf.…”

Section: Introductionmentioning

confidence: 99%

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

et al. 2020

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Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.

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Monitoring of crop fields using multispectral and thermal imagery from UAV

Cited by 99 publications

References 4 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Uso de imagens tomadas por aronaves remotamente pilotadas para detecção da cultura do milho infestadas por Spodoptera frugiperda

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

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