Leaf Wetness Research Articles

Foliar water uptake and phyllosphere microbe colonization increase under higher soil nitrogen availability

Leaf water uptake (FWU) represents an alternative pathway to plant water acquisition that can have positive effects on water and carbon balance. Leaf surface traits including the phyllosphere microbes can affect the leaf wetness capacity and FWU. These functional and structural leaf traits could change depending on soil resources availability. The aim of this study was to evaluate the responses of FWU and leaf surface traits such as contact angle, water drop adhesion (LWA) and phyllosphere-associated microbiota to soil nitrogen addition. Three dominant plant species, Azorella prolifera, Senecio filaginoides, and Papostippa speciosa, of an arid steppe in Patagonia exposed to nitrogen (+N) and nitrogen plus water (+NW) addition for ten years were selected. Leaf contact angle did not exhibit statistical differences among treatments within species. LWA was higher in all treatments with respect to the control (C) for shrub A. prolifera and grass P. speciosa. Nitrogen addition increased significantly FWU in A. prolifera and in P. speciosa with respect to C. Colony-forming units of culturable microorganisms (CFU) on leaf surface responded to N addition, but the changes were statistically significant in S. filaginoides and P. speciosa in +NW, increasing three and eight times, respectively, in relation to the C. A positive linear relationship was found between FWU and LWA across species and treatments. On the other hand, CFU of phyllosphere was negative and exponentially correlated with LWA and FWU, across species and treatments. The results suggest that soil N enrichment could affect functional leaf traits and phyllosphere microbiota in a way that may confer a higher potential to cope with drought by facilitating the use of alternative water sources. On the other hand, we suggested that species with leaves more colonized have less surface exposed for FWU and could have lower wettability depending on the hydrophobicity degree of microbes. However, a higher cover of epiphyte’s microorganisms could compensate the effects of lower FWU by avoiding the leaf dehydration. This study contributes to a better understanding of plant leaf-microbe interactions under higher N atmospheric deposition and intensive fertilization as global agricultural production is expected to increase.

Similar freezing spectra of particles in plant canopies and in the air at a high-altitude site

Abstract. Plant canopies are an important source of biological particles aerosolized into the atmosphere. Certain aerosolized microorganisms are able to freeze slightly supercooled cloud droplets and therefore affect mixed-phase cloud development. Still, spatiotemporal variability of such biological ice-nucleating particles (INPs) is currently poorly understood. Here, we study this variability between late summer and leaf shedding on the scale of individual leaves collected about fortnightly from four temperate broadleaf tree species (Fagus sylvatica, Juglans regia, Prunus avium and Tilia platyphyllos) on a hillside (Gempen, 650 m a.s.l. (metres above sea level)) and in a vertical canopy profile of one Fagus sylvatica (Hölstein, 550 m a.s.l.) in north-western Switzerland. The cumulative concentration of INPs active at ≥-10 °C (INPs−10) did not vary significantly between the investigated tree species but, as inferred from leaf mass per area and leaf carbon isotopic ratios, seemed to be lower on sun leaves as compared with shade leaves. Between August and mid-November, the median INP concentration increased from 4 to 38 INP−10 cm−2 of leaf area and was positively correlated with mean relative humidity throughout 24 h prior to sampling (Spearman's r=0.52, p<0.0001, n=64). In 53 of the total 64 samples collected at the Gempen site, differential INP spectra between −3 and −10 °C exhibited clearly discriminable patterns: in 53 % of the spectra, the number of additionally activated INPs increased persistently with each 1 °C decrease in temperature; the remaining spectra displayed significant peaks in differential INP concentration above −9 °C, most frequently in the temperature interval between −8 and −9 °C (21 %) and between −7 and −8 °C (17 %). Interestingly, the three most frequent patterns in differential INP spectra on leaves in Gempen were also prevalent in similar fractions in air samples with clearly discriminable patterns at the high-altitude Jungfraujoch site (3580 m a.s.l., Switzerland) collected during summer in the previous year. These findings corroborate the idea that a large fraction of the airborne biological INP population above the Alps during summer originates from plant surfaces. Which parameter or set of parameters could affect biological INP populations on both scales – upwind airsheds of high-altitude sites as well as individual leaves – is an intriguing question for further exploration. A first guess is that leaf wetness duration plays a role.

Epidemiology of boxwood blight in western North Carolina and Virginia and evaluation of the boxwood blight infection risk model.

Boxwood blight, caused by Calonectria pseudonaviculata, is a highly invasive emerging disease. Since the first US report in North Carolina and Connecticut in 2011, boxwood blight has spread to over 30 US states, risking more than 90% of boxwood production. Our study investigated the disease field epidemiology and evaluated the boxwood blight infection risk model's prediction by analysing weekly blight monitoring data collected on detector plants exposed to the prevailing environmental conditions at two different locations (western Virginia and North Carolina) from spring through fall of 2014 to 2017. Boxwood blight was recorded in 61 of 86 weeks, with the highest infected leaf counts recorded in late summer or early fall. Rainfall, high relative humidity outside rainy periods and optimal temperatures (13.6-22.7°C) during prolonged leaf wetness (> 65h per week) had a significant positive effect on boxwood blight development. Classification analyses showed that disease predictions from the model using leaf wetness estimated by leaf wetness sensor were more closely aligned with observations from the field than predictions based on algorithms. This study improved our understanding of disease field epidemiology, provided leads to improve the existing model, and generated essential knowledge for formulating effective strategies for blight mitigation.

Design and Implementation of Leaf Wetness Sensor with different Graphene Oxide Concentration

Design and Implementation of Leaf Wetness Sensor with different Graphene Oxide Concentration

Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data

AbstractAccurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha−1, where DM is dry matter) and increased the R2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.

Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.

Monitoring quantities of Phyllosticta citricarpa inoculum in sweet orange orchards using qPCR and young citrus plants as spore traps

AbstractMonitoring of Phyllosticta citricarpa (causal agent of citrus black spot [CBS]) inoculum in orchards has previously been performed using volumetric spore traps. However, volumetric traps are nonspecific, and only capture ascospores (not pycnidiospores) of different Phyllosticta species. This research aimed to monitor and quantify the DNA of P. citricarpa using young citrus plants as a spore trap combined with quantitative PCR (qPCR), as well as quantify the number of ascospores and pycnidiospores, and to correlate results with weather variables. Citrus nursery plants were placed as traps under and outside tree canopies during fruit developmental stages (from October to July) in two seasons in commercial ‘Valencia’ sweet orange orchards. DNA was extracted from trap leaves to quantify P. citricarpa inoculum by qPCR of the ribosomal internal transcribed spacer 1 (ITS1 rRNA) region of P. citricarpa (Pc‐ITS). Correlations of Pc‐ITS to rainy days, leaf wetness and temperature were performed. Overall, the highest numbers of 400 Pc‐ITS/cm2 of leaf tissue, which represented up to 12 ascospores or pycnidiospores per cm2, were detected on leaves sampled from October to March, regardless of the trap position, season and orchard. Trap plants placed under canopies had up to 20‐fold more Pc‐ITS than those placed outside. Rainy days and leaf wetness were the variables most positively correlated with Pc‐ITS. Both results in the fluctuation of P. citricarpa inoculum in orchards and the most favourable weather variables associated with inoculum production contribute to better understanding of the critical periods for CBS management in citrus‐growing areas.

Impact of Electrode Patterns Variation on the Response Characteristic of Leaf Wetness Sensors

Impact of Electrode Patterns Variation on the Response Characteristic of Leaf Wetness Sensors

Progression of Gummy Stem Blight Epidemics on Watermelon with and without Fungicide Inputs.

Gummy stem blight (GSB), caused primarily by the fungus Stagonosporopsis citrulli in the southeastern United States, affects cucurbits and is particularly destructive on watermelon. Previous epidemiological models of GSB constructed for greenhouse cucumber showed leaf wetness and temperature were the primary and secondary environmental factors, respectively, that explained epidemic progress. The objective of this study was to construct a model that predicted GSB severity on field-grown watermelon based on environmental factors. Disease and weather data from six fungicide experiments in Charleston, South Carolina, in spring and fall 1997 and fall 2017, 2018, 2019, and 2022 were used as inputs. Fungicide treatments were grouped into nonsprayed, protectant (chlorothalonil and mancozeb) and GSB-specific (cyprodinil, difenoconazole and fludioxonil) applications. Cumulative hours of leaf wetness was the primary explanatory variable that modeled the increase in proportion GSB severity ≥2% across all epidemics. Incorporation of temperature or other environmental variables did not improve the model. Fit of the overall model was evaluated with k-fold cross validation, where individual experiments were each excluded from the model fitting process. Slopes of predicted disease progress curves were lowered significantly compared to the nonsprayed treatments by applications of protectant fungicides. Applying GSB-specific fungicides alternated with chlorothalonil further reduced slope values. The model successfully predicted progress of GSB epidemics under different weather patterns and fungicide applications.

Management Strategies for Early Blight in Potatoes: Assessment of the TOMCAST Model, Including the Aerobiological Risk Level and Critical Phenological Period

The use of pesticides is an efficient approach for pest management. However, their increasing application in recent decades has come under the spotlight of world policies. In this context, this study addresses the usefulness of a forecasting model (TOMCAST) combined with aerobiological information and a plant development model (physiological days, PDays) for the control of early blight in potatoes in Northwest Spain. Control plots were compared to treated plots, according to the original TOMCAST model and the daily Alternaria spp. concentration, meteorological factors, and phenological and epidemiological observations were monitored for better adjustment of the TOMCAST model to the weather conditions of the geographical area during three crop seasons. The results of the linear regression analysis showed a strong relationship between the parameters included in TOMCAST (leaf wetness and temperature) and the Alternaria spp. conidia concentration. In addition, an unbalanced pattern of trapped conidia was detected throughout the growing season, with an increase near the flowering stage. The epidemiological parameters (infection period, r-AUDPC, maximum severity value, and total and commercial yields) showed significant differences between the cultivars in the control and the TOMCAST plots in terms of r-AUDPC and the maximum severity value. Given the study’s results, the original TOMCAST model was improved with aerobiological and phenological information. The improved model recommends a first spray on a day when the following three requirements are met: Ten accumulated disease severity values (DSVs) according to the TOMCAST model, two days with an aerobiological level greater than 10 conidia/m3, and a PDays value greater than 200. This will reduce the number of fungicide treatments used to control early blight in potato crops, promoting the principles of sustainable agriculture.

Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard

Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics.

Utilizing High-Resolution Imaging and Artificial Intelligence for Accurate Leaf Wetness Detection for the Strawberry Advisory System (SAS).

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, FL, USA, and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.

Asian rust of soybean favorability Mato Grosso-Brazil

This study presents the map that indicates favorable environmental conditions for the development of Asian soybean rust in Mato Grosso, for a given period. It’s about a plant's disease caused by the fungus Phakopsora pachyrhizi, which has soybean as its main host. By affecting the plantation, anticipates the ripening of the grains, which can considerably reduce your productivity. The map is the reproduction, for Mato Grosso, of the image developed by Alves et al. (2011) for the state of Minas Gerais, generated from a fuzzy logic-based rule system. The input variables for this system are temperature and leaf wetness duration, while the output is the area below the disease progression curve. To supply it, data were collected from automatic meteorological stations located in the state and neighboring regions. Records were made available hourly for January 2019. In possession of these results, for interpolation were tested the inverse distance weighting and kriging models. The method that showed the best results was the first, with an exponent equal to two.

Reducing flea-beetle feeding wounds on canola seedlings with foliar insecticide failed to improve blackleg control

In earlier studies, it was found that Leptosphaeria maculans could readily infect canola via fresh wounds but would require extended leaf wetness to infect intact tissues. We hypothesized that flea beetle feeding wounds might facilitate L. maculans infection under field conditions. Multi-year plot trials were conducted in western Canada to assess the effect of reducing flea beetle feeding wounds on canola seedlings with in-crop insecticide on subsequent blackleg infection. The experiment encompassed susceptible and resistant cultivars seeded into canola stubble from the previous year, with pyrethroid insecticide applied weekly commencing at trace feeding (<5%) to mitigate the insect damage. Fluopyram seed treatment was used as an additional treatment against early blackleg infection. Results from eight station years showed moderate feeding damage by flea beetles and generally high blackleg pressure, with the average disease incidence > 80%. Despite reduced feeding damage due to in-crop insecticide treatment, significant blackleg reduction was not observed. A greenhouse study revealed no correlation between puncture wounds on cotyledons and successful stem infection under high-inoculum conditions, suggesting that reduced insect feeding might not effectively counteract the heavy inoculum pressure resulting from canola residue in field trials. Fluopyram seed treatment did not reduce blackleg substantially, while resistant cultivars consistently exhibited effectiveness. Furthermore, harvested seed from plots with lighter blackleg often displayed lower levels of L. maculans inoculum contamination.

Sheath Blight of rice: A review of host plant interaction and disease management

The devastating pathogen Rhizoctonia solani, which is responsible for significant quality and production losses worldwide, is discussed in this review article along with the symptoms, disease cycle, epidemiology which causes sheath blight disease in rice. R. solani Kuhn, poses a significant threat to tropical Asia's rice production, potentially reducing grain yield by 50%. The article also emphasises the numerous management techniques, such as cultural, biological, and chemical controls. It is a severe production bottleneck in high yielding rice varieties under intensive rice production techniques, monoculture methods, a dense canopy, and extensive nitrogen management. Disease propagation in fields is influenced by air temperature, moisture content, and leaf wetness, with 16-25 C temperature range and 90% humidity favoring growth. Vertical and horizontal spread are influenced by these factors. The article provides comprehensive insight on host plant interaction of pathogen R. solani.

Non-contact leaf wetness measurement with laser-induced light reflection and RGB imaging

Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whereas water reflects it. Based on this difference, an experimental platform was built to semi-automatically measure droplet deposition on grape leaves while capturing red laser images using an RGB camera. The setup measured changes in leaf mass and area of scanned leaves to determine the water mass per leaf area as a measure of leaf wetness. A sprayer was used to apply water droplets to the leaves. As the amount of deposited water increased, the mean red channel intensity decreased, with more bright spots in the images. These bright spots were more distinguishable as droplets in the green channel. Segmented leaf area, mean red channel intensity, and the number of identified droplets were used as image features. A generalised additive model was employed to predict the leaf wetness value with extracted features. The R-squared value for the prediction of the validation dataset was 0.71. Image resolution and leaf orientation were identified as factors that influenced the model accuracy. The measurement method introduced in this study shows potential for accurately quantifying leaf wetness, and implies that in practice detecting leaf wetness can be integrated into a multi-classification problem, thereby broadening the potential applications of optical methods.

Utilizing Disease Prediction Models to Time Fungicide Applications for Controlling Ripe Rot, Caused by Colletotrichum spp., in Maryland Vineyards.

Two previously published ripe rot prediction models, DF2-NN and GH2-DT, were evaluated for fungicide application timing efficacy in Maryland vineyards. Both models utilize leaf wetness duration (LWD), temperature, and grape cluster phenological stages as model parameters. These three parameters were tracked throughout the 2021 to 2023 seasons in three vineyards. The fungicide efficacy trials started at the veraison phenological stage and included a nontreated control, a 12-day interval treatment, and two model-triggered treatments when risk predicted by the models crossed a threshold. The severity of ripe rot on the clusters in each treatment was assessed when the fruit were mature. Ripe rot severity in the nontreated controls was higher during seasons with more LWD and more precipitation. Days in which the models were triggered by the environmental conditions primarily coincided with precipitation events and lengthy LWDs. The model-triggered treatments never had significantly higher ripe rot severity than the 12-day interval treatment but had significantly lower severities than the nontreated control in most trials which had high ripe rot pressure. Furthermore, the model-triggered treatments resulted in fewer fungicide applications than the 12-day interval treatment on average. The DF2-NN model appeared to be more accurate and useful for ripe rot prediction and treatment than the GH2-DT model because it triggered fewer fungicide applications while reducing ripe rot. This model may be useful for improving or maintaining ripe rot control with fewer fungicide inputs, which may be beneficial for the environment and the reduction of fungicide resistance selection.

The combined effect of diffuse radiation and leaf wetness on functional traits and transpiration efficiency on a cloud forest species.

Cloud forests are unique biomes that thrive in foggy environments for a substantial part of the season. Fog in cloud forests plays two critical roles: it reduces incoming radiation and creates a humid environment, leading to the wetting of the canopy. This paper aims to investigate the combined effect of both radiation and wetness on Myrica faya Wilbur-a cloud forest species present in subtropical regions-both directly in plants and through simulations. Experiments consisted of a controlled environment with two levels of radiation and leaf wetness: low radiation/wet conditions, and high radiation/no-wetness; and three treatments: continuous low radiation and wetness, continuous high radiation and no wetness and alternate high low radiation and alternate wetness. The results revealed that a combination of low radiation and leaf wetness significantly improves leaf stomata conductance and increases the specific leaf area (SLA). Changes in SLA were driven by leaf size changes. However, the minimum leaf conductance (gmin) did not respond to any of the treatments. The simulations focused on exploring the impact of radiation and canopy wetness on transpiration efficiency (TE), i.e. the ratio between photosynthesis (An) and transpiration (Tc). The simulations demonstrated that TE increased exponentially as the canopy was gradually wetted, regardless of the radiation environment. This increase in TE results from Tc approaching zero while An maintains positive values. Overall, this study provides an integrated understanding of how fog alters M. faya functioning and, potentially, other cloud forest tree species.

Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness

Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light–matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of 12,000 distinct water droplet patterns on a plastized leaf was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.

Identifying the Source of Water on Plant Using the Leaf Wetness Sensor and via Deep Learning-Based Ensemble Method

Identifying the Source of Water on Plant Using the Leaf Wetness Sensor and via Deep Learning-Based Ensemble Method