Study of microclimatic conditions under different environments in wheat crop

An experiment was cunducted to study the wheal microclimate for higher grain yield production at research farm of Hisar 'Agriculture’ University (HAU). Profiles of temperature relative humidity, leaf temperature leaf wetness wind speed were measured in the crop canopies of different sowing treatments. Higher air temperatures and lower soil moisture were observed during reproductive phase under delayed sowings. Albedo varied between 0.19 and 0.23. Test weight decreased with delay in sowing time. Sowing of wheat between 31 October and 14 November produced statistically higher yield under favourable microclimate conditions at dirrerent phenophases.

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.

Qualitative Parameter Analysis for Botrytis cinerea Forecast Modelling Using IoT Sensor Networks

This paper presents the evaluation of a fungal disease forecast model in vineyards for qualitative parameter analysis using the data from off the shelf sensors, i.e. temperature and air relative humidity, rain precipitation, and leaf wetness. The rules for the fungal disease models are digitalized as a decision support tool that serve as an indicator to farmers for the need of spraying of the chemical substances to ensure the best growing condition and suppress the level of parasites. The temperature and humidity contexts are used interchangeably in practice to detect the risk of the disease occurrence. By taking into account a number of influences on these parameters collected from the shelf sensors, new topics for research in the multidimensional field of precision agriculture emerge. In this study, the impact of the humidity is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each humidity parameter has it&rsquo;s own threshold that triggers the second step of the disease modeling - risk index based on the temperature. The research showed that for humidity a low-cost relative humidity sensor can detect in average 14.61% risk values, a leaf wetness sensor an additional 3.99% risk cases, and finally, a precipitation sensor will detect only an additional 0.59% risk cases.

Evaluation of Two Predictive Models for Forecasting Olive Leaf Spot in Northern Greece

Olive leaf spot (Venturia oleaginea) is a very important disease in olive trees worldwide. The introduction of predictive models for forecasting the appearance of a disease can lead to improved disease management. One of the aims of this study was to investigate the effect of temperature and leaf wetness on conidial germination of local isolates of V. oleaginea. The results showed that a temperature range of 5 to 25 °C was appropriate for conidial germination, with 20 °C being the optimum. It was also found that at least 12 h of leaf wetness was required to start the germination of V. oleaginea conidia at the optimum temperature. The second aim of this study was to validate the above generic model and a polynomial model for forecasting olive leaf spot disease under the field conditions of Potidea Chalkidiki, Northern Greece. The results showed that both models correctly predicted infection periods. However, there were differences in the severity of the infection, as demonstrated by the goodness-of-fit for the data collected on leaves of olive trees in 2016, 2017 and 2018. Specifically, the generic model predicted lower severity, which fits well with the incidence of the disease symptoms on unsprayed trees. In contrast, the polynomial model predicted high severity levels of infection, but these did not fit well with the incidence of disease symptoms.

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.