Search Results (21)

Maize production is increasingly challenged by climate change, which affects plant physiology, fungal colonization, and mycotoxin contamination. Aspergillus flavus, a saprophytic fungus, thrives in warm, dry conditions, leading to aflatoxin B1 (AFB1) accumulation, and posing significant food safety risks. Macro- and micro-climatic factors, including temperature, humidity, and precipitation, influence kernel development, leaf wetness duration, and mycotoxin biosynthesis. Nitrogen availability and irrigation play crucial roles in modulating plant responses to these stressors, affecting chlorophyll content, yield parameters, and fungal interactions. To investigate these interactions, a Completely Randomized Design (CRD) was employed from 2020 to 2022 to assess physiological changes in SY Orpheus maize hybrid under varying climatic conditions. Rising temperatures and declining relative humidity (RH) significantly reduced kernel number per ear length from 25.60 ± 0.34 in 2020 to 17.89 ± 0.39 in 2022 (p < 0.05), impacting yield. The AFB1 levels peaked in 2021 (156.88 ± 59.02 µg/kg), coinciding with lower humidity and increased fungal stress. Water availability improved kernel numbers and reduced AFB1 accumulation (p < 0.05) but did not significantly affect the total fungal load (p > 0.05). Nitrogen supplementation enhanced plant vigor, suppressed AFB1 biosynthesis, and influenced spectral indices. Potential confounding factors such as soil variability and microbial interactions may require further investigations. Full article

Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and predicted leaf wetness duration (LWD) intervals based on meteorological conditions, can help growers to anticipate and manage crop diseases effectively. Effective crop disease management programs integrate crop rotation, tillage practices, hybrid selection, and fungicides. However, growers often struggle with correctly timing fungicide applications, achieving only a 30–55% positive return on investment (ROI). This paper describes the development of a disease-warning and fungicide timing system, equally effective at predicting NLB and GLS with ~70% accuracy, that utilizes historical and forecast hourly weather data. This scalable recommendation system represents a valuable tool for proactive, practicable crop disease management, leveraging in-season weather data and advanced modeling techniques to guide fungicide applications, thereby improving profitability and reducing environmental impact. Extensive on-farm trials (>150) conducted between 2020 and 2023 have shown that the predicted fungicide timing out-yielded conventional grower timing by 5 bushels per acre (336 kg/ha) and the untreated check by 9 bushels per acre (605 kg/ha), providing a significantly improved ROI. Full article

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. Full article

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. Full article

Peach leaf curl is a fungal disease caused by Taphrina deformans, and it can severely affect the health and productivity of peach and nectarine trees (Prunus persica) if left unmanaged. This study was carried out to investigate the temperature and wetness conditions that affect the germination of blastospores and ascospores of local isolates of the fungus T. deformans. The results showed that the rate of both ascospore and blastospore germination was reduced as the temperature increased from 0 to 20 °C. A decrease in temperature from the range of 25 °C to 30 °C caused a reduction in the germination of conidia for both ascospores and blastospores. Ascospore and blastospore germination were totally inhibited at −3 and 35 °C. Under constant temperatures of 20 °C, the percentage of both ascospore and blastospore germination of T. deformans gradually increased as the wetness period increased from 9 to 15 h. However, there was no further increase in germination observed beyond the 15 h wetness period. Additionally, this study aimed to validate the predictive models of T. deformans, developed based on the favorable temperatures and leaf wetness durations, under the specific field conditions of Naoussa, Greece. The results indicate that while both the ascosporic and blastosporic models were capable of correctly predicting infection periods, there were differences in their predictions of infection risk. The ascosporic model predicted lower risk infection, which aligned well with the observed symptoms of the disease. In contrast, the blastosporic model predicted higher risk infection, but this did not match the actual intensity of the symptoms. Finally, this study also provided insights into the potential benefits of using predictive models to guide fungicide applications, potentially leading to more targeted and efficient disease management strategies for commercial peach orchards. Full article

Controlled-release fertilizer is one of the best fertilizer management strategies for improving the yield and nitrogen use efficiency of transplanted seedling rice. Wet direct-seeded rice has gradually replaced transplanted seedling rice since it saves labor. In addition, it is conducive to mechanization promotion. However, the effects of controlled-release fertilizers on wet direct-seeded rice remain largely unknown. A two-year field experiment aimed to compare the effects of controlled-release blended fertilizer at two rates (basal N to tiller N ratio = 7:3 (CRBF+U), CRBF alone), urea at two rates (basal–tiller ratio of 4:6 (U₄₀), 6:4 (U₆₀)) and a control (no N fertilizer) on the ammonia volatilization (AV) loss, nitrogen runoff loss, accumulation, transport, utilization and yield of rice. The nitrogen runoff loss in wet direct-seeded rice paddy fields was concentrated from sowing to the three-leaf and one-leaflet stage, and the loss rat was lowest after CRBF+U (11.41–12.94%). AV loss rate was lowest after CRBF (3.41%), followed by CRBF+U (3.55–3.89%). CRBF+U increased nitrogen accumulation by extending the duration of rapid nitrogen growth and accelerating maximum nitrogen growth. CRBF+U also increased the nitrogen transport rate of stems, sheaths and leaves from full heading to maturity, and intensified the increase in nitrogen in panicles, increasing the harvest index, agronomy utilization rate and apparent utilization rate of nitrogen. Finally, the grain number per panicle, seed-setting rate and actual yield of rice were significantly improved. In conclusion, CRBF+U can reduce nitrogen runoff loss and AV loss and can improve the yield and nitrogen use efficiency of wet direct-seeded rice. Full article

Bacterial blight, caused by Xanthomonas arboricola pv. juglandis, is a significant disease affecting walnut production worldwide. Outbreaks are most severe in spring, and closely tied to host phenology and weather conditions. Pathogen infections are mainly observed in catkins, female flowers, leaves, and fruits. In this study, the effect of wetness duration and temperature on walnut infections by X. arboricola pv. juglandis was determined through two independent experiments conducted under controlled environmental conditions. The combined effect of both climatic parameters on disease severity was quantified using a third-order polynomial equation. The model obtained by linear regression and backward elimination technique fitted well to the data (R² = 0.94 and R²_adj = 0.93). The predictive capacity of the forecasting model was evaluated on pathogen-inoculated walnut plants exposed to different wetness duration–temperature combinations under Mediterranean field conditions. Observed disease severity in all events aligned with predicted infection risk. Additionally, the relationship between leaf and fruit age and the disease severity was quantified and modelled. A prediction model, which has been referred to as the WalBlight-risk model, is proposed for evaluation as an advisory system for timing bactericide sprays to manage bacterial blight in Mediterranean walnut orchards. Full article

Blossom and twig blight, caused by Monilinia spp., is the main disease in apricot trees. In this study, we installed transparent rain shelters in apricot orchards to study their influence on the modification of the microclimate at the level of the tree canopy and on the reduction in moniliosis damage in twigs. Rain shelters significantly reduced the leaf wetness time measured within the foliage compared to the unsheltered trees (a reduction of between 43% and 67%). However, very few differences were observed in the daily averaged air temperature (up to 6%) and daily averaged air relative humidity (up to 1%). In the first experiment, on the apricot variety Bergarouge^® (CEP Innovation, Lyon, France), moniliosis damage on twigs in the absence of phytosanitary protection was reduced by up to 62% for the trees provided with rain protection compared to the trees that did not receive rain shelters. A second experiment, involving five apricot tree varieties, made it possible to verify that fungicide protection could be reduced for the trees protected by rain covers, reducing moniliosis damage on twigs compared to full fungicide protection combined without rain protection. Finally, a third experiment comprising two apricot tree varieties has shown that in organic orchards, rain protection provides protection against moniliosis (twig blight) that is equivalent to an organic farming fungicide protection programme based on the use of copper sulphate and calcium polysulphide. Full article

The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS disease risk modeling. By accurately measuring the LWD, disease risk can be better assessed, leading to less fungicide use and more economic benefits to the farmers. This research aimed to develop and test a more accurate leaf wetness detection system than traditional leaf wetness sensors. In this research, a leaf wetness detection system was developed and tested using color imaging of a reference surface and a convolutional neural network (CNN), which is one of the artificial-intelligence-based learning methods. The system was placed at two separate field locations during the 2021–2022 strawberry-growing season. The results from the developed system were compared against manual observation to determine the accuracy of the system. It was found that the AI- and imaging-based system had high accuracy in detecting wetness on a reference surface. The developed system can be used in SAS for determining accurate disease risks and fungicide recommendations for strawberry production and allows the expansion of the system to multiple locations. Full article

Grape downy mildew (GDM) is a destructive grapevine disease caused by Plasmopara viticola that occurs worldwide. In this study, we determined the characteristics of GDM epidemics and the grapevine canopy micro-climate in open-field, fungicide-spray, and rain-shelter plots during two constitutive years (2016 and 2017). It was found that rain shelter can significantly delay the disease occurrence by 28 and 21 days, reduce the epidemic phase by 28 and 21 days, and decrease the final disease index by 82% and 83%. Furthermore, it can block precipitation, reduce the relative humidity by 11% and 8%, and reduce the leaf wetness duration by 85% and 76% compared with open-field cultivation. A total of 3861, 783, and 1145 lesions were collected from the open-field, fungicide-managed, and rain-shelter plots, respectively, for analyses of the genetic diversity, population differentiation, and epidemic mode with seven microsatellite markers. In terms of genetic diversity, the Nei’s diversity index ranged from 0.569 to 0.680 and Shannon’s information index ranged from 0.958 to 1.226, showing high levels of diversity across populations. Similar to fungicide management, a rain shelter can significantly reduce the population’s genetic diversity. Low pairwise FST values (0.003–0.047) and high gene flow (Nm = 1.548–20.699) were observed among the three populations each year. In addition, most of the genetic variation occurred within populations. The epidemic mode of GDM in the open-field, fungicide-managed, and rain-shelter cultivation showed moderate, low, and high levels of clonality, respectively, in the case study. Full article

Downy mildew is, globally, one of the most significant diseases in viticulture. Control of this pathogen is achieved through fungicide application. However, due to restrictions (from upcoming regulations) and growing environmental conscientiousness, it is critical to continuously enhance forecasting models to reduce fungicide application. Infection potential has traditionally been based on a 50 h–degree calculation (temperature multiplied by leaf wetness duration) measured by weather stations; the main climatic parameters for forecast modelling are temperature, relative humidity, and leaf wetness. This study took these parameters measured by a weather station and compared them with the same parameters measured inside a grape canopy. The study showed that the temperature readings by the weather station compared to inside the canopy recorded differences during the day but not at night; the relative humidity showed significant differences during both daytime and night; leaf wetness showed the highest differences and was statistically significant during both daytime and night. In conclusion, the measurement differences between inside of the canopy and at the weather station have significant impacts on the precision of forecasting models. Thus, using data from inside of a canopy for the prediction should lead to even less fungicide applications. Full article

Late detection of fungal infection is the main cause of inadequate disease control, affecting fruit quality and reducing yield of grapevine. Therefore, infrared imagery as a remote sensing technique was investigated in this study as a potential tool for early disease detection. Experiments were conducted under field conditions, and the effects of temporal and spatial variability in the leaf temperature of grapevine infected by Plasmopara viticola were studied. Evidence of the grapevine’s thermal response is a 3.2 °C increase in leaf temperature that occurred long before visible symptoms appeared. In our study, a correlation of R² = 0.76 at high significance level (p ≤ 0.001) was found between disease severity and MTD. Since the pathogen attack alters plant metabolic activities and stomatal conductance, the sensitivity of leaf temperature to leaf transpiration is high and can be used to monitor irregularities in temperature at an early stage of pathogen development. Full article

Plant phenology is the most sensitive biological indicator that responds to climate change. Many climate models predict that extreme precipitation events will occur frequently in the arid areas of northwest China in the future, with an increase in the quantity and unpredictability of rain. Future changes in precipitation will inevitably have a profound impact on plant phenology in arid areas. A recent study has shown that after the simulated enhancement of precipitation, the end time of the leaf unfolding period of Nitraria tangutorum advanced, and the end time of leaf senescence was delayed. Under extreme climatic conditions, such as extremely dry or wet years, it is unclear whether the influence of the simulated enhancement of precipitation on the phenology of N. tangutorum remains stable. To solve this problem, this study systematically analyzed the effects of the simulated enhancement of precipitation on the start, end and duration of four phenological events of N. tangutorum, including leaf budding, leaf unfolding, leaf senescence and leaf fall under extremely dry and wet conditions. The aim of this study was to clarify the similarities and differences of the effects of the simulated enhancement of precipitation on the start, end and duration of each phenological period of N. tangutorum in an extremely dry and an extremely wet year to reveal the regulatory effect of extremely dry and excessive amounts of precipitation on the phenology of N. tangutorum. (1) After the simulated enhancement of precipitation, the start and end times of the spring phenology (leaf budding and leaf unfolding) of N. tangutorum advanced during an extremely dry and an extremely wet year, but the duration of phenology was shortened during an extremely wet year and prolonged during an extremely drought-stricken year. The amplitude of variation increased with the increase in simulated precipitation. (2) After the simulated enhancement of precipitation, the start and end times of the phenology (leaf senescence and leaf fall) of N. tangutorum during the autumn advanced in an extremely wet year but was delayed during an extremely dry year, and the duration of phenology was prolonged in both extremely dry and wet years. The amplitude of variation increased with the increase in simulated precipitation. (3) The regulation mechanism of extremely dry or wet years on the spring phenology of N. tangutorum lay in the different degree of influence on the start and end times of leaf budding and leaf unfolding. However, the regulation mechanism of extremely dry or wet years on the autumn phenology of N. tangutorum lay in different reasons. Water stress caused by excessive water forced N. tangutorum to start its leaf senescence early during an extremely wet year. In contrast, the alleviation of drought stress after watering during the senescence of N. tangutorum caused a delay in the autumn phenology during an extremely dry year. Full article

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. Full article

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. Full article