Influence of temperature and daily leaf wetness duration on the severity of bacterial leaf blight of garlic

ABSTRACT In the present study, climate control chamber conditions were adopted to investigate the influence of temperature (10, 15, 20, 25 and 30°C) and leaf wetness duration (6, 12, 24 and 48 hours) on the severity of bacterial leaf blight of garlic, caused by Pseudomonas marginalis pv. marginalis. The relative density of lesions was influenced by temperature and leaf wetness duration (P<0.05). The disease was more severe at 20°C. The obtained data underwent non-linear regression analysis. Generalized beta function was used to fit the data on severity and temperature, while a logistic function was chosen to represent the effect of leaf wetness duration on the severity of bacterial blight. The response surface resulting of the product of those two functions was expressed as ES = 0.019419 * (((x-5)0.5893) * ((35-x)0.5474)) * (0.51754/(1+23.59597* exp (-0.145695*y))), where: ES represents the estimated severity value (0.1); x, the temperature (ºC), and y, the daily leaf wetness duration (hours). This model shall be validated under field conditions to assess its use as a forecast system for bacterial leaf blight of garlic.

Approaches for the Prediction of Leaf Wetness Duration with Machine Learning

The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min.

Emulators of a Physical Model for Estimating Leaf Wetness Duration

Leaf wetness duration (LWD) has rarely been measured due to lack of standard protocol. Thus, empirical and physical models have been proposed to resolve this gap. Although the physical model provides robust performance in diverse conditions, it requires many variables. The empirical model requires fewer variables; nevertheless, its performance is specific to a given condition. A universal LWD estimation model using fewer variables is thus needed to improve LWD estimation. The objective of this study was to develop emulators of the LWD estimation physical model for use as universal empirical models. It is assumed that the Penman–Monteith (PM) model determines LWD and can be employed as a physical model. In this study, a simulation was designed and conducted to investigate the characteristics of the PM model and to build the emulators. The performances of the built emulators were evaluated based on a case study of LWD data obtained in South Korea. It was determined that a machine learning algorithm can properly emulate the PM model in LWD estimations based on the simulation. Moreover, the poor performances of some emulators that use wind speed may have been due to the limitation of wind speed measurement. The accuracy of the anemometer is thus critical to estimating LWD using physical models. A deep neural network using relative humidity and air temperature was found to be the most appropriate emulator of those tested for LWD estimation.

The Role of Ascospores and Conidia, in Relation to Weather Variables, in the Epidemiology of Stemphylium Leaf Blight of Onion

Stemphylium leaf blight (SLB), caused by Stemphylium vesicarium, is an important foliar disease of onion in northeastern North America. The pathogen produces conidia and ascospores, but the relative contributions of these spore types to epidemics in onion is not known. An effective disease forecasting model is needed to predict disease risk and to time fungicide applications. Determining the abundance of ascospores and conidia during the growing season couldwill contribute to a disease forecasting model. Air-borne ascospores and conidia of S. vesicarium were trapped during the growing season of 2015 and 2016 at an onion trial site in southern Ontario, Canada, using a Burkard 7-day volumetric sampler. Meteorological data wereas recorded hourly. Ascospore numbers peaked before the crop was planted and declined rapidly with time and at daily mean air temperatures > 15 °C. Conidia were present throughout the growing season and appear to be closely related to the development of SLB on onion. Daily spore concentrations were variable, but 59 to 73% of ascospores and ~60% of conidia were captured between 0600 to1200 h. Spore concentrations increased 24 to 72 h after rainfall and . Other variables associated with moisture, such as precipitation and leaf wetness duration, were consistently and positively associated with increases in numbers of conidia and subsequent SLB incidence . The first symptoms of SLB coincided with high numbers of conidia, rainfall, leaf wetness duration ≥ 8 h and days with average daily temp ≥ 18°C oC. The number of air-borne ascospores was very low by the time SLB symptoms were observed. Ascospores may initiate infection on alternative hosts in early spring, while conidia are the most important inoculum or the epidemic on onions.

Influence of Leaf Wetness Duration and Temperature on Infection of Grape Leaves by Elsinoë ampelina under Controlled and Vineyard Conditions

On susceptible varieties, indirect damage to vines infected by Elsinoë ampelina range from reduced vigor to complete defoliation while, on berries, damage ranges from reduced quality to complete yield loss. Limited knowledge about the relationship between weather conditions and infection makes anthracnose management difficult and favors routine application of fungicides. The influence of leaf wetness duration and temperature on infection of grape leaves by E. ampelina was studied under both controlled and vineyard conditions. For the controlled conditions experiments, the five youngest leaves of potted vines (Vidal) were inoculated with a conidia suspension and exposed to combinations of six leaf wetness durations (from 0 to 24 h) and six constant temperatures (from 5 to 30°C). A week after each preset infection period, the percent leaf area diseased (PLAD) was assessed. At 5°C, regardless of the leaf wetness duration, no disease developed. At 10 and at 15 to 30°C, the minimum leaf wetness durations were 4 and 6 h, respectively. Above the minimum wetness duration, at temperatures from 10 to 30°C, PLAD increased linearly, with increasing leaf wetness up to 12 h, and then at a lower rate from 12 to 24 h. The optimal temperature for infection was 25°C. Relative infection was modeled as a function of both temperature and wetness duration using a Richards model (R2 = 0.93). The predictive capacity of the model was evaluated with data collected in experimental vineyard plots exposed to natural wetness durations or artificial wetness durations created using sprinklers. In total, 264 vineyard infection events were used to validate the controlled experiments model. There was a linear relationship between the risk of infection estimated with the model and the observed severity of anthracnose (R2 = 90); however, the model underestimated disease severity. A risk chart was constructed using the model corrected for vineyard observations and three levels of risk, with light, moderate, and severe risks corresponding to ≤5, >5% to ≤25, and >25% leaf area diseased, respectively. Overall, 93.9% of 132 independent observations were correctly classified, with 100, 29.4, and 9.4% of the light, moderate, and severe risks, respectively.

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea.