Recent Developments in Methods for Assessing Disease Losses in Forage/Pasture Crops

Nutter, Forrest W.; Gaunt, R. E.

doi:10.2134/1996.pastureforagecroppathol.c6

Cited by 20 publications

(16 citation statements)

References 33 publications

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“…For seed corn field inspectors, knowledge concerning the spatial pattern of this disease may help support the development of more efficient sampling designs for this disease (Campbell and Madden, 1990). At present, seed corn field inspectors are simply instructed to "thoroughly" inspect each seed corn field, but are not given specific instructions as to what sampling design or sampling pattern to employ (Nutter and Gaunt, 1996). Finally, the effect of insecticide seed treatment on the temporal and spatial patterns of P. stewartii beyond the V5 growth stage, is not known.…”

Section: Pathogen Expansion Between the Veins Lesion Expansion Betweementioning

confidence: 99%

Quantifying the aggressiveness, temporal and spatial spread of <i>Pantoea stewartii </i> in sweet corn

Liu

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Section: Pathogen Expansion Between the Veins Lesion Expansion Betweementioning

confidence: 99%

Quantifying the aggressiveness, temporal and spatial spread of <i>Pantoea stewartii </i> in sweet corn

Liu

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“…Foliar diseases caused by fungi can lead to severe losses as well as significant reductions in forage quality (10,11,13,34,99). Yield loss (also referred to as the "yield gap"), is defined as the difference between the attainable yield and the actual yield achieved by farmers (77,84). A general definition for attainable yield is the yield achieved when all available crop protection tactics are deployed in an attempt to minimize the impacts of all biotic pests, whereas actual yield is defined as the yields achieved by farmers using current crop protection practices (84).…”

Section: Alfalfa Diseases and Yield Lossesmentioning

confidence: 99%

“…Disease assessment is the process of quantitatively measuring disease intensity in a host population (77,83). Traditionally, visual disease assessment has been the approach most often employed by plant pathologists to assess disease injury (14,72,76).…”

Section: Disease Assessmentmentioning

confidence: 99%

“…These instruments indirectly assess the amount of disease injury in crops by measuring the reduction in healthy green leaf area caused by plant pathogens (72,76). The biotic stress caused by plant pathogens can cause many changes in plant canopies, such as leaf and stem lesions, leaf blight, yellowing, premature defoliation, wilting, stunting, and premature senescence (76,77). While many of these responses are difficult to visually quantify with acceptable levels of accuracy, precision, and speed, plant responses to plant pathogen stress can also affect the amount and quality of electromagnetic radiation reflected and emitted from plant canopies (72,76,77).…”

Section: Disease Assessmentmentioning

confidence: 99%

“…The biotic stress caused by plant pathogens can cause many changes in plant canopies, such as leaf and stem lesions, leaf blight, yellowing, premature defoliation, wilting, stunting, and premature senescence (76,77). While many of these responses are difficult to visually quantify with acceptable levels of accuracy, precision, and speed, plant responses to plant pathogen stress can also affect the amount and quality of electromagnetic radiation reflected and emitted from plant canopies (72,76,77). Thus, remote sensing instruments that measure and record changes in electromagnetic radiation may provide a better means to objectively quantify disease injury (stress) (75,78,80).…”

Section: Disease Assessmentmentioning

confidence: 99%

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Assessing disease stress and modeling yield losses in alfalfa

Guan¹

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This dissertation consists of six chapters. There are two sections in the first chapter; the first section is a general introduction where the research problems and objectives are discussed. The second section is an extensive literature review where previous research concerning alfalfa diseases, disease assessment methods, and remote sensing methods are presented. Chapters 2 through 5 are in the form of journal papers. The second chapter addresses the effects of environmental factors (incident radiation, sun angle, leaf wetness, and sensor height) on remote sensing assessments. This new quantitative information was used to recommend a new assessment protocol that should improve the accuracy and the precision of remote sensing assessments. The third chapter quantifies and compares the inter-rater reliabilities and intra-rater repeatabilities of visual disease versus remote sensing assessment methods. The fourth chapter presents a comparison or single point and area under the curve (AUC) yield models using visual disease or remote sensing assessments as the independent variable. The fifth chapter quantifies the relationships between percentage defoliation, dry weight, percentage reflectance, and green leaf area index. The sixth chapter summarizes and discusses the implications of the research results achieved in this project. Literature Review Alfalfa diseases and yield losses Alfalfa diseases. Fungi, bacteria, nematodes, viruses, and phytoplasmas can all cause disease in alfalfa. Alfalfa pathogens and the diseases they cause are summarized by '' LAI = leaf area index. Asterisk indicates slopes are statistically different from zero at the P < 0.01 level. Day of Application Percentage Disease Disease Location Year vear Funeicide frequency defoliation incidence severity-I99S 113 Mancozcb Twice 41.56 ab 98.16 a I7.4S a 113 Propiconazolc Twice 39.89 ab 97.66 a 15.80 ab 113 Cupric Hydroxide Twice 39.51 ab 97.78 a 16.08 ab 113 Chlorothalonil Twice 37.35 b 97.59 a 16.43 ab 113 Mancozeb Once 36.34 b 98.32 a 16.66 a 113 Propiconazolc Once 41.02 ab 97.46 a 13.66 ab 113 Cupric Hydroxide Once 37.64 b 96.44 a 15.36 ab 113 Chlorothalonil Once 42.10 ab 98.52 a 13.85 ab 113 CGA-245704 Twice 40.22 ab 97.90 a 17.30 a 113 Non-treated control None 45.57 a 98.24 a 11.53 b

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Measuring plant disease severity in R: introducing and evaluating the pliman package

Olivoto

Andrade

Ponte

2022

Trop. plant pathol.

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Image analysis based on color thresholding is the reference method for measuring severity as percent area affected. It is deemed to produce accurate results, usually considered the "true" severity value. More than a dozen applications have been used for the task in phytopathometry studies, but none was coded in R language. Here we introduced and evaluated pliman, a suite for the analysis of plant images. In particular, we show functions for computing percent severity based on RGB information contained in image palettes prepared by the user. Six image collections, totaling 249 images, from different diseases (wheat tan spot, soybean rust, olive leaf spot, rice brown spot, bean angular spot, and Xyllela fastidiosa on tobacco) exhibiting a range of symptomatic patterns and severity were used to evaluate the agreement of pliman predictions with APS Assess, LeafDoctor and ImageJ. Three users independently prepared three image palettes (each representing leaf background, symptomatic or healthy leaf tissue) by manually inspecting and subsetting these target areas in the images. Pliman predictions by a joint palette (by joining images by the three users into one) were highly concordant (ρ c > 0.98) with measures by the other software for all but Xylella fastidiosa on Tobacco (ρ c = 0.49). The error for the latter may be due to the low contrast between symptomatic and healthy tobacco tissues. Users showed to be a source of variation in the overall concordance depending on the disease. Reduction in the image resolution (< 1 megapixel) did not impact the results. Combined with parallel processing, the use of low image resolution sped up the processing time resulting in pliman being ~170 to ~430 times faster than existing tools for disease quantification. Pliman showed great potential to produce accurate measures and accelerate studies involving plant disease severity measurements, especially for the batch processing of large sets of image collections.

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Recent Developments in Methods for Assessing Disease Losses in Forage/Pasture Crops

Cited by 20 publications

References 33 publications

Quantifying the aggressiveness, temporal and spatial spread of <i>Pantoea stewartii </i> in sweet corn

Quantifying the aggressiveness, temporal and spatial spread of <i>Pantoea stewartii </i> in sweet corn

Assessing disease stress and modeling yield losses in alfalfa

Measuring plant disease severity in R: introducing and evaluating the pliman package

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