Digital Image Analysis of Old World Bluestem Cover to Estimate Canopy Development

Xiong, Yedan; West, Charles P.; Brown, Claire; Green, P.E.

doi:10.2134/agronj2018.08.0502

Cited by 27 publications

(33 citation statements)

References 13 publications

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“…Canopeo measurement of green cover (%) correlated with biomass across different crops and growth stages in our dataset (dotted line calculated with the model of Xiong et al 2019; black line fitted model to measurements made with Samsung Galaxy A6 smartphone).…”

Section: Figmentioning

confidence: 96%

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Smartphone‐based estimation of green cover depends on the camera used

Heinonen

Mattila

2021

Agronomy Journal

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Smartphone based visual assessment of vegetation cover is a promising, fast and repeatable approach that allows land managers to compare measurement on their farm with other farms. This study determined the influence of the smartphone device on green cover measurements on several crops. The hypothesis was that different smartphones would provide similar green cover (reflectance) for management purposes (i.e. < 10% difference).Forty fields in Finland were sampled between 10 and 28 July 2020 with Motorola Moto G7 and Samsung Galaxy A6 smartphones. The results were compared also to Sentinel-2 remote sensing of NDVI (Normalized Difference Vegetation Index) and biomass quadrants. The two smartphones had different green reflectance values that were correlated to each other.Both green reflectance measurements correlated with the normalized difference vegetation index (NDVI) that was measured with the Sentinel 2 satellite sensor and biomass. These finding suggest that smartphone-based monitoring can be used at least to classify vegetation to low, medium and high density, but that results from different cameras should not be compared.

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Section: Figmentioning

confidence: 96%

“…The different methods correlate strongly and Canopeo has been considered to be an accurate and objective tool for e.g. pasture monitoring and research (Büchi et al, 2018;Jáuregui et al, 2019;Xiong et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Smartphone‐based estimation of green cover depends on the camera used

Heinonen

Mattila

2021

Agronomy Journal

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“…ImageJ (http://imagej.nih.gov) is an open‐source, Java‐based software available online. It has been successful in estimating forage grass canopy cover in OWB pastures (Xiong, 2014) and in quantifying variation in perennial ryegrass ( Lolium perenne L.) cover among different locations (Lynch et al, 2015). Rayburn (2014) described a new application of DIA using photo‐point counts in Microsoft PowerPoint (Microsoft, Redmond, WA) to estimate legume content in orchardgrass ( Dactylis glomerata L.), red clover ( Trifolium pratense L.), and white clover ( Trifolium repens L.) pasture.…”

Section: The Need For Rapid Objective Predictions Of Legume Content mentioning

confidence: 99%

Nondestructive Determination of Legume Content in Grass‐Legume Pastures

Baxter¹,

West²,

Brown³

et al. 2017

Crop Forage & Turfgrass Mgmt

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Core Ideas Procedures were more successful when forage species were distinct hues of green. Overall, predictive accuracy of PowerPoint model was best in Old World bluestem–legume pasture. Only the visual model showed potential in tall wheatgrass–alfalfa pasture. To better manage forage productivity and quality, it is important to accurately and objectively monitor legume content in grass‐legume pastures. Current nondestructive methods are too subjective or labor intensive. Recent research has led to developments in digital image analysis (DIA) and remote sensing techniques useful in pasture research and management. Four nondestructive sampling techniques were compared with traditional botanical hand separations to determine their ability to predict legume content in pastures of tall wheatgrass [Thinopyrum ponticum (Podp.); TW] and alfalfa (Medicago sativa L.): TW‐alfalfa; and in pastures of Old World bluestem [Bothriochloa bladhii (Retz) S.T. Blake; OWB], alfalfa, and yellow sweetclover (Melilotus officinalis L.; YSC): OWB‐legume. Each sampling procedure better estimated and predicted legume content in OWB‐legume pasture (R2cal = 0.40 to 0.78; R2pred = 0.40 to 0.78) than in TW‐alfalfa (R2cal = 0.02 to 0.61; R2pred = 0.08 to 0.61). Overall, the best predictive accuracy was obtained with the PowerPoint model in OWB‐legume pasture. Only the visual model showed potential in TW‐alfalfa because these two species were difficult to distinguish on‐screen by DIA methods or using spectral thresholds. The use of nondestructive sampling techniques to measure legume content is promising for mixed pastures when the grass and legume components are distinct hues of green. Although the need for more research is recognized, the ultimate goal is to apply these findings to automated scanners that offer producers rapid measurements of legume content in mixed pastures.

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“…The app analyses and classifies all pixels in the image according to ratios of R/G, B/G, and the excess green index (2G-RB), and then reports the average percentage of green canopy cover (%GCC) of the image. Compared with other software packages, Canopeo® uses an automatic colour threshold classification, which dramatically reduces the processing time of each picture compared to manual methods (Xiong et al 2019). The user can preview images and then adjust the noise-reduction threshold to better represent reality.…”

Section: Introductionmentioning

confidence: 99%

Determining yield of forage crops using the Canopeo mobile phone app

JÃ¡uregui

Delbino

Bonvini

et al. 2019

Journal of NZ Grasslands

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One of the biggest challenges in pastoral systems is accurately determining forage availability. Destructive sampling is the most accurate method to use but it is very time-consuming, so non-destructive methods are usually preferred. This study compared data on the percentage of green canopy cover (%GCC) obtained using a recently developed image-analysis mobile phone app (Canopeo®) with crop biomass, light interception and NDVI measurements. Data were obtained for a range of crop species from two dryland experiments of lucerne (E1) and winter forage crops (E2). Linear and quadratic regression models were constructed to evaluate the data obtained using Canopeo® to estimate biomass accumulation, light interception and NDVI. For lucerne, positive linear relationships were found between %GCC and biomass (0.77 and 0.79 for Spring + Summer and Autumn + Winter, respectively; P<0.05), and between %GCC and light interception (R2=0.83; P<0.05). For winter forage crops, there was a linear relationship between biomass and %GCC (R2=0.81; P<0.05), and a linear relationship between %GCC and light interception (R2=83; P<0.05). NDVI data obtained using the GreenSeeker® crop sensing system was linearly related to %GCC data from Canopeo® in both experiments (R2=0.84 in E1 and R2=0.93 in E2; P<0.05). The Canopeo® app proved to be a fast and reliable method to estimate biomass accumulation and light interception of forage crops in this study and has potential wider applications. However, it does not distinguish between crops and weeds.

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Digital Image Analysis of Old World Bluestem Cover to Estimate Canopy Development

Cited by 27 publications

References 13 publications

Smartphone‐based estimation of green cover depends on the camera used

Smartphone‐based estimation of green cover depends on the camera used

Nondestructive Determination of Legume Content in Grass‐Legume Pastures

Determining yield of forage crops using the Canopeo mobile phone app

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