Batch Analysis of Digital Images to Evaluate Turfgrass Characteristics

Karcher, Douglas E.; Richardson, Michael D.

doi:10.2135/cropsci2004.0562

Cited by 180 publications

(148 citation statements)

References 3 publications

(4 reference statements)

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“…The collected data were classified to determine the canopy coverage, which was defined as the proportion of image pixels that were canopy pixels to the total number of pixels for any given field plot. The canopy coverage data of the ground-based imageries were determined using the software SigmaScan Pro ® (SYSTAT, San Jose, CA, USA), which implements the method described by Karcher and Richardson [22]. The canopy coverage data of aerial imageries were obtained by the software ENVI 5.0 TM (Harris Geospatial Solutions, Boulder, CO, USA) using a binomial model.…”

Section: Canopy Coverage and Imagery Data Collectionmentioning

confidence: 99%

“…For the aerial method, the highest correlations (0.33 and 0.44) were reached on days 45 and 41, respectively. To assess the usefulness of canopy coverage data in the early stages of the vegetative growth, the information from days [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] (for the four data sets) was included in the prediction models, and the results were compared with those obtained using canopy information from the whole season. …”

Section: Relationship Between Canopy Coverage Data and Grain Yieldmentioning

confidence: 99%

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Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

et al. 2018

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Abstract:Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14-71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27-123%, 27-148%, and 65-165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14-33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season.

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Section: Canopy Coverage and Imagery Data Collectionmentioning

confidence: 99%

Section: Relationship Between Canopy Coverage Data and Grain Yieldmentioning

confidence: 99%

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

et al. 2018

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“…If cultural practices were performed on the same date, photographs were taken first. Digital images were analyzed with SigmaScan Pro software (version 5.0; SPSS) using a SigmaScan Pro macro named "Turf Analysis" (22) for batch analysis of the digital images. The threshold settings (22) were adjusted to hue = 0 to 53 and saturation = 0 to 57.…”

mentioning

confidence: 99%

“…Digital images were analyzed with SigmaScan Pro software (version 5.0; SPSS) using a SigmaScan Pro macro named "Turf Analysis" (22) for batch analysis of the digital images. The threshold settings (22) were adjusted to hue = 0 to 53 and saturation = 0 to 57. These threshold settings allowed for estimation of pixels (expressed as percentages) that represented non-green turf relative to healthy (green) turf.…”

mentioning

confidence: 99%

Effect of Cultivation and Timing of Nitrogen Fertilization on Large Patch Disease of Zoysiagrass

Obasa¹,

Fry²,

Bremer³

et al. 2013

Plant Disease

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Obasa, K., Fry, J., St. John, R., and Kennelly, M. 2013. Effect of cultivation and timing of nitrogen fertilization on large patch disease of zoysiagrass. Plant Dis. 97:1075-1081.Large patch of zoysiagrass (Zoysia spp.) is caused by Rhizoctonia solani anastomosis group 2-2 LP. The effects of summer cultivation (core-aerification, verticutting, and sand topdressing) and spring and fall versus summer nitrogen (N) fertilization on large patch in fairway height 'Meyer' zoysiagrass were investigated from 2008 to 2011 in Manhattan, Haysville, and Olathe, KS. Disease was assessed by measuring patch diameters or analyzing digital images of affected plot areas to determine the percentage of non-green turfgrass within patches. Cultivation did not affect thatch temperature, soil temperature, soil water content, or turf recovery from large patch in early summer. Furthermore, cultivation did not result in overall significant reductions in patch diameters or average weekly rate of patch diameter increase among plots at the three experimental locations. In some site-year combinations, spring and fall N fertility was associated with lower percentages of non-green turf within affected plot areas in Manhattan and Haysville. In some cases, applications of N during spring and fall may alleviate large patch symptoms.Zoysiagrass (Zoysia japonica Steud. and Z. matrella (L.) Merr.) is a warm-season (C4), perennial turfgrass that is widely used on golf courses in the "transition-zone" of the United States, a region that includes Kansas and states eastward to Virginia and North Carolina. Compared with certain cool-season (C3) turfgrass species used on fairways, such as creeping bentgrass (Agrostis stolonifera L.), zoysiagrass has lower water, fertilizer, and pesticide requirements while maintaining a high-quality surface (16). Large patch, caused by Rhizoctonia solani Kühn anastomosis group (AG) 2-2 LP, is the most common and most severe disease of zoysiagrass.In Kansas, symptoms of large patch appear during spring (April to May) and autumn (September to October) as roughly circular, slightly matted tan patches with bright orange margins (17,40,45). Patches can range from 1 to 6 m or more in diameter (17,45), with healthy turf sometimes scattered within the patches. Symptoms on individual plants occur as reddish-brown to black lesions on the basal leaf sheaths. During summer conditions, regrowth within patches often results in full turf recovery, though weeds can encroach while the zoysiagrass is thinned.In laboratory tests, a temperature range of 15 to 25°C was optimum for infection of zoysiagrass by the fungus (17). It has been suggested that the lack of symptoms during summer months is because thatch and soil temperatures often exceed 30°C, potentially hindering growth of the fungus while, at the same time, favoring zoysiagrass root and shoot growth (17). Additionally, soil moisture and leaf wetness are important factors in large patch development and severity. Severe large patch symptoms on zoysiagrass have been associated wi...

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“…2010, by taking photographs of each plot within a frame of 1 m 2 that had been laid over the plot. The photo sections within this square meter were then analysed with the SigmaScan Pro 5 "Turf Analysis" program (Systat Software Inc., San Jose, USA) (Karcher, Richardson 2005). A colour threshold was set and the percentage of green pixels was calculated.…”

Section: Methodsmentioning

confidence: 99%

New strawberry genotypes tested for organic production on a Verticillium-infested site

Weissinger¹,

Flachowsky²,

Spornberger³

2014

Hort. Sci. (Prague)

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Weissinger H., Flachowsky H., Spornberger A., 2014. New strawberry genotypes tested for organic production on a Verticillium-infested site. Hort. Sci. (Prague), 41: 167-174.Nineteen new strawberry breeding clones, bred at the Institute for Breeding Research on Fruit Crops in Dresden-Pillnitz, Germany, were evaluated for their suitability for organic production at a Verticillium-infested site in Vienna, Austria. Soil cover, plant vitality, resistance to leaf spot diseases as well as flower damages by the strawberry blossom weevil (Anthonomus rubi) and by spring frost were recorded. In two consecutive years, marketable yield and fruits infested by Botrytis cinerea were assessed. Three breeding clones, P-7189, P-8043 and P-8071, were considered as suitable for organic strawberry production. P-7189 and P-8043 showed high tolerance to Verticillium wilt. The breeding clones P-8155 and P-8166 performed well under organic management but had very soft fruits that were not acceptable for commercial fruit production. The rest of the breeding clones were low-yielding, however, some of them could be of interest for home gardens due to their good flavour and high plant vitality.

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Batch Analysis of Digital Images to Evaluate Turfgrass Characteristics

Cited by 180 publications

References 3 publications

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

Effect of Cultivation and Timing of Nitrogen Fertilization on Large Patch Disease of Zoysiagrass

New strawberry genotypes tested for organic production on a Verticillium-infested site

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