Analysis of Incidence–Severity Relationships for Strawberry Powdery Mildew as Influenced by Cultivar, Cultivar Type, and Production Systems

Carisse, Odile; Lefebvre, Annie; Heyden, Hervé Van der; Roberge, L.; Brodeur, Luc

doi:10.1094/pdis-05-12-0508-re

Cited by 25 publications

(20 citation statements)

References 23 publications

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“…Based on this analysis, when disease incidence ranged from 0.25 to 0.50, the predicted ACC values ranged from 16.1 to 63.8 conidia/m\ respectively. Considering that an action threshold for fungicide application of 5% leaf area diseased, which corresponds to disease incidence of 0.35 (5), was suggested to avoid damage to berries (6), we selected ACC thresholds of 50 and 100 conidia/m"*, which could be considered as warning and action threshold for postharvest treatments, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…because their fruits are produced over a long period (9), generally from mid-July to the end of September (which is when epidemics are generally severe), as well as in strawberry plants grown under plastic tunnels, because the weather conditions in tunnels are generally favorable to disease development (5,22,26). The disease also causes indirect losses through leaf infection by reducing the photosynthetic potential of leaves and, hence, reducing plant vigor.…”

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confidence: 99%

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Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

et al. 2014

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Van der Heyden, H., Lefebvre, M., Roberge, L., Brodeur, L., and Carisse, O. 2014. Spatial pattern of strawberry powdery mildew (Podosphaera aphanis) and airborne inoculum. Plant Dis. 98:43-54.The relationship between strawberry powdery mildew and airborne eonidium concentration (ACC) of Podosphaera aphanis was studied using data collected from 2006 to 2009 in 15 fields, and spatial pattern was described using 2 years of airborne inoculum and disease incidence data collected in fields planted with the June-bearing strawberry (Fragaria x ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3x8 grids containing 24 100 m-quadrats. Variance-tomean ratio, index of dispersion, negative binomial distribution. Poisson distribution, and binomial and beta-binomial distributions were used to characterize the level of spatial heterogeneity. The relationship between percent leaf area diseased and daily ACC was linear, while the relationship between ACC and disease incidence followed an exponential growth curve. The V/M ratios were significantly greater than 1 for 100 and 96% of the sampling dates for ACC sampled at 0.35 m from the ground (ACCo 35^) and for ACC sampled at 1.0 m from the ground (ACCiom). respectively. For disease incidence, the index of dispersion D was significantly greater than 1 for 79% of the sampling dates. The negative binomial distribution fitted 86% of the data sets for both ACC| om and ACCoijn,. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets.

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Section: Resultsmentioning

confidence: 99%

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Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

et al. 2014

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“…In this study, the RGB map mosaicked from UAV data had a ground resolution of 6 cm, which made it possible distinguish canopy from background components and assess disease severity (DS) and disease incidence (DI) for every vineyard quickly. DS indicates the severity of the disease, and DI was the proportion of diseased rows in a plot [40,41]. A value 0 was used to represent no incidence and 1 was used to represent incidence.…”

Section: Study Site and Field Data Collectionmentioning

confidence: 99%

UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

et al. 2021

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Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSEVIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation.

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“…These datasets can also feed back into an enhanced understanding of modified varieties' real-world performance in relation to microclimates, mycorrhizal fungi, pests and diseases (Camprubi et al 2007;Xu et al 2012). Powdery mildew infection risk, for example, is likely to be predictable using environmental data (Carisse et al 2013). In the case of this strawberry variety, Malling Centenary, RH was the strongest predictor of field yields and phenology.…”

Section: 5 Agronomic Implicationsmentioning

confidence: 99%

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Lee

Monteiro²,

Barclay

et al. 2019

Preprint

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Predicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive.In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional withinpolytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R 2 = 0.6) and RH (R 2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R 2 = 0.8). We present a framework for using machinelearning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.

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Analysis of Incidence–Severity Relationships for Strawberry Powdery Mildew as Influenced by Cultivar, Cultivar Type, and Production Systems

Cited by 25 publications

References 23 publications

Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

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