Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Lamb, David; Trotter, Mark; Schneider, Derek

doi:10.1016/j.compag.2009.07.004

Cited by 25 publications

(12 citation statements)

References 12 publications

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“…At present, research institutions around the world have gained access to reflection spectra of crop canopies obtained using various devices (e.g., Cropscan multispectral radiometers [12], ASD FieldSpec 3 hyper-spectrometers [13,14], GreenSeeker sensors [15,16], and CropCircle ACS-470) [17]. The research undertaken has indicated out that there is a good correlation between the reflection spectra of crop canopies and crop nutrients.…”

Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

Sensors

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In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

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Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

Sensors

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“…Active sensors are calibrated initially in the lab, and operate independently of the sun; they may be operated day or night (Lamb et al 2009). Currently available active sensors require close proximity to the target due to the light source intensity.…”

Section: Introductionmentioning

confidence: 99%

“…Passive sensors have been used from satellites, manned aircraft and unmanned aerial vehicles (UAVs) (Berni et al 2009). Active sensors have been vehicle-mounted, handheld or used on manned aircraft (Lamb et al 2009). To date, active sensors have not been mounted or integrated into unmanned aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

et al. 2017

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“…Both proximal sensors are normally used to monitor cover crops to manage nitrogen fertilization according to the logic of variable rate application (Govaerts et al, 2007;Sripada et al, 2008). These devices were proposed to monitor crop vigour variations as an alternative to the acquisition of multispectral images (Lamb et al, 2009); in particular, Drissi et al (2009) and Mazzetto et al (2009) suggested the application of the GreenSeeker to monitor vigour variations of Vitis vinifera plants in vineyards. Previous experiences were carried out by the authors in field using a mobile lab equipped with GreenSeeker sensor, in order to evaluate its efficiency in detecting the dynamic diffusion of disease incidence directly in vineyard (Mazzetto et al, 2010(Mazzetto et al, , 2011.…”

Section: Resumen Evaluación De Sensores óPticos Para El Diagnóstico Dmentioning

confidence: 99%

Evaluation of “ground sensing” optical sensors for diagnosis of Plasmopara viticola on vines

Calcante

Mena

Mazzetto

2012

Span. j. agric. res.

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The present work considers the possible use of two commercial optical devices, the GreenSeeker RT100 and the Crop Circle, in detecting different levels of grapevine downy mildew symptoms. The analysis was conducted on vine leaves that had been picked from plants of cv. Cabernet Franc infected by Plasmopara viticola. Leaves were divided into eight homogeneous infection classes and then analyzed (on the leaves' adaxial surfaces) through the optical devices and a portable visible/near infrared (Vis/NIR) spectrophotometer used as tester. Data showed a linear relation between the percentage of symptomatic leaf area and the Normalized Difference Vegetation Index (NDVI) calculated through the optical sensors (R 2 = 0.708 for GreenSeeker; R 2 = 0.599 for Crop Circle; R 2 = 0.950 for the spectrophotometer). The regression obtained for GreenSeeker is more significant than the one obtained for Crop Circle. This fact suggests a greater capability of GreenSeeker than Crop Circle in detecting different disease levels and its possible use in diagnosis application in the vineyard. Finally, the NDVI measurements carried out through the two commercial sensors, showed lower values on abaxial surfaces than on adaxial surfaces, and a reduced range of values. Moreover, the identification of different infection classes was more difficult on the abaxial surface. This is due to both the different structure of the leaf tissue and the different symptoms of P. viticola on the abaxial and adaxial surfaces. The present work will allow, in the future, applying these optical devices to diagnosis directly in vineyards.Additional key words: Crop Circle; diagnosis of phytopathogenic fungi; GreenSeeker; NDVI; plant health status; proximal sensing. Resumen Evaluación de sensores ópticos para el diagnóstico de Plasmopara viticola en videsEl presente estudio describe la posibilidad de utilizar dos sensores ópticos comerciales, el GreenSeeker RT100 y el Crop Circle, para identificar diferentes niveles de síntomas de mildiu en vid. La experimentación ha sido realizada en hojas de vid cv. Cabernet Franc infectadas por Plasmopara viticola. Las hojas se dividieron en ocho clases homogéneas por nivel de infección y analizadas (en las caras superiores) con instrumentos ópticos y con un espectrofotómetro Vis/ NIR (visible/infrarrojo), utilizado como testigo. Los resultados muestran la presencia de una regresión lineal entre el porcentaje de superficie foliar con síntomas y el índice NVDI calculado por medio de los sensores ópticos (R 2 = 0,708 para GreenSeeker; R 2 = 0,599 para Crop Circle; R 2 = 0,950 para el espectrofotómetro). La regresión obtenida con el GreenSeeker es más significativa que la obtenida con el Crop Circle. Esto sugiere una mayor capacidad del GreenSeeker para detectar diferentes niveles de infección. Por último, las mediciones realizadas por medio de los dos sensores comerciales en el envés de las hojas mostraron valores de NVDI menores que los obtenidos en la cara superior, así como un menor rango de valores. Por lo tanto, la ide...

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Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Cited by 25 publications

References 12 publications

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Evaluation of “ground sensing” optical sensors for diagnosis of Plasmopara viticola on vines

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