Comparing a Single-Sensor Camera With a Multisensor Camera for Monitoring Coffee Crop Using Unmanned Aerial Vehicles

Gomes, Amanda Pereira Assis; Queiroz, Daniel Marçal de; Valente, Domingos Sárvio Magalhães; Pinto, Francisco de Assis de Carvalho; Rosas, Jorge Tadeu Fim

doi:10.1590/1809-4430-eng.agric.v41n1p87-97/2021

Cited by 5 publications

(9 citation statements)

References 18 publications

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“…He used a multispectral camera with five sensors and another camera with only one sensor. It turns out that the NDVI obtained with the multisensor camera is closer to the NDVI obtained with the GreenSeeker NDVI sensor [12].…”

Section: Introductionmentioning

confidence: 51%

Multisensor Speech Enhancement Technology in Music Synthesizer Design

Peng

2022

Mobile Information Systems

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Creating music through sound synthesis is the most representative electronic music creation method, and electronic music is actually the result of sound synthesis technology. Today, the field of electronic music encompasses multiple areas such as recording, mixing, composing, and producing. It also has some advantages over traditional music composition. Voice is the most effective and direct way of communication between people. And with the explosive development of speech recognition technology, the recognition rate of speech recognition systems in the near field environment has been greatly improved. However, in practical applications, there is often a large amount of ambient noise. If these environmental noises are strong, it will seriously affect the quality, accuracy, and speed of music synthesis. This greatly reduces not only the sound quality and clarity of speech but also the speed of speech recognition. To solve these problems, this paper proposes a multisensor speech enhancement technique and implements a multisensor speech enhancement system. It also proposes an enhancement method based on speaker speech and microphone speech. In this paper, the low-frequency harmonic components of the bone conduction signal are used to replace the frequency points disturbed by wind noise to reduce the influence of wind noise on speech quality and intelligibility. The experimental results show that the PESQ and MOS scores of the improved algorithm in this paper are 1.65 and 3.67, respectively. Compared with the existing methods, it has a great improvement. This can effectively improve the voice quality of the music synthesizer and reduce background noise.

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Section: Introductionmentioning

confidence: 51%

Multisensor Speech Enhancement Technology in Music Synthesizer Design

Peng

2022

Mobile Information Systems

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“…Percebem-se de forma bem definida a presença da estrada em todas as imagens, sendo um excelente algo para identificação dos valores de NDVI representativo da classe de solo exposto. Gomes et al (2021) avaliaram o NDVI resultante de câmera Micasense e Mapir, e observaram que a maioria dos pontos avaliados apresentaram valores de NDVI mais elevados quando determinado por meio da câmera Micasense. Segundo esses autores, isso se deve a vários fatores, dentre os quais, as próprias características da câmera, que utiliza uma mesma lente para capturar todas as imagens.…”

Section: íNdice De Vegetação Por Diferença Normalizada (Ndvi)unclassified

Avaliação do índice de área foliar e índice de área da planta em floresta seca utilizando modelos simplificados em imagens de alta resolução com o uso de VANT

Santos

Moura²,

Carvalho³

et al. 2022

Jour. Hypers. Rem. Sensg.

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O sensoriamento remoto tem possibilitado a aplicação de modelos para estimar variáveis ambientais, dentre eles o índice de área foliar (LAI) e o índice de área da planta (PAI), importantes para avaliação da sazonalidade da vegetação, principalmente em florestas secas. Assim, objetivou-se avaliar o LAI e PAI na caatinga usando imagens aéreas de alta resolução obtidas com um veículo aéreo não tripulado (VANT). Em área de caatinga preservada foram realizados voos com o VANT acoplado com c âmeras RGB e RGN. Utilizou-se modelos para estimativa do LAI e PAI tendo como parâmetro de entrada o NDVI. Dados de LAI e PAI a partir do satélite Landsat-8 foram usados para comparação entre os produtos obtidos pelo VANT. A avaliação do NDVI ocorreu por regressão linear (R 2 =0,993), obtendo NDVI médio da Caatinga de 0,14 e 0,38 com os dados Landsat -8 nos períodos seco e chuvoso; 0,12 e 0,07 com a câmera RGB e RGN do VANT nos períodos seco e 0,65 e 0,27 para período chuvoso. Os dados LAI e o PAI (m 2 m -2 ) representaram bem a área em estudo, obtendo R 2 =0,992 e R 2 =0,993 para LAI e PAI, respectivamente. O LAI médio da Caatinga foi 0,19 (período seco) e 0,80 (período chuvoso) pelo Landsat-8; 0,26 e 0,14 com a câmera RGB e RGN do VANT nos períodos secos e 2,18 e 0,48 para o período chuvoso. Já o PAI, os valores médios foram 1,39 e 2,02 com os dados Landsat -8 nos períodos seco e chuvoso; 1,46 e 1,34 com a câmera RGB e RGN do VANT nos períodos seco e 3,42 e 1,69 para o período chuvoso. Desse modo, os modelos calculados com imagens VANT para estimativa do LAI e do PAI da caatinga podem ser aplicados em imagens de alta resolução espacial obtidas em câmeras multiespectrais acopladas em VANT, o btendo resultados satisfatórios.

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“…but it is necessary to verify the accuracy of spectral measurements made by these sensors. Despite being successfully used in studies (Lebourgeois et al 2008; Argolo dos Santos et al 2020), some authors reported lower measurement accuracy for infrared converted cameras, if compared to multi-sensor cameras or spectroscopes (Bueren et al 2015;Gomes et al 2021;Nijland et al 2014). This might partially be because the bands captured by single-sensor RGB camera is usually sensitive to light outside of the target wavelengths, and so measurements in speci c band may be polluted by light in other parts of the spectrum (Burggraaff et al 2019;Berra et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Before such cameras can be recommended for operational use on farms, it is important to verify that spectral measurements and vegetation indices derived from the infrared converted camera correspond well to measurements made by other 'proper' multispectral cameras, or hand-held spectrometers. The study by Gomes et al (2021) investigated this dynamic for the Mapir Survey3W commercial infrared converted camera, by comparing it to the multispectral MicaSense RedEdge-MX camera. The study calculated the vegetation index NDVI of a coffee plantation using both cameras, as well as a handheld NDVI sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The current study reinvestigates the question of the suitability of current commercial infrared converted cameras for use in vegetation condition monitoring. To improve continuity between research, we present a case-study that considers the same multispectral camera, handheld NDVI sensor and infrared converted camera used by the study of (Gomes et al 2021). Our experiment differs however in the calibration technique used, speci c spectral lter used in the infrared converted camera, and also the crop type that was captured.…”

Section: Introductionmentioning

confidence: 99%

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On the accuracy of infrared-converted drone cameras for use in vegetation and environmental monitoring

Louw

Chen

Avtar

2023

Preprint

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Drones equipped with cameras sensitive to near-infrared wavelengths are increasingly being used in environmental assessment studies and in agriculture. These cameras can measure vegetation cover, extent of eutrophication in water bodies, and aspects of crops, such as growth vigour, biomass and potential yield. Infrared converted cameras that capture near-infrared wavelengths offer a low-cost alternative to multi-sensor multispectral cameras or drone-borne spectrometers. However, some studies point to lower accuracy in measurements by such infrared converted sensors. So, to what extent can infrared converted cameras be used to quantify vegetation condition? This study compared vegetation index measurements (NDVI) from an infrared converted camera to measurements by a multispectral camera and a handheld NDVI meter, captured over soybean and potato fields. It was observed that infrared converted camera derived NDVI was consistently lower over crop than multispectral and handheld based measurements. However, correlation between the sensor values were high (r = 0.95, r = 0.87 for respective survey days). This suggests that the infrared converted sensor is valuable for qualitative assessment of vegetation status across a farm. Based on the result of this study we however recommend caution when using infrared converted camera for quantitative applications like calculating fertiliser prescription rates from vegetation index maps. We discuss possible reasons for the lower vegetation index measurements observed, noting overestimation of reflectance in the red band, but underestimation in the near-infrared band, leading to low NDVI values.

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Comparing a Single-Sensor Camera With a Multisensor Camera for Monitoring Coffee Crop Using Unmanned Aerial Vehicles

Cited by 5 publications

References 18 publications

Multisensor Speech Enhancement Technology in Music Synthesizer Design

Multisensor Speech Enhancement Technology in Music Synthesizer Design

Avaliação do índice de área foliar e índice de área da planta em floresta seca utilizando modelos simplificados em imagens de alta resolução com o uso de VANT

On the accuracy of infrared-converted drone cameras for use in vegetation and environmental monitoring

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