Estimation of soybean leaf wetness from meteorological variables

Abstract

The objective of this work was to determine models for the estimation of leaf wetness percentage at three heights in the soybean (Glycine max) canopy, using meteorological variables from stations installed at the crop site and at an agrometeorological station. The experiment was conducted in three harvest seasons, in an area cropped with soybean, in the municipality of Londrina, in the state of Paraná, Brazil. To collect the meteorological variables, electronic trees were installed at four heights (0.3, 0.6, 0.9, and 1.7 m) in the crop and a station was installed in an agrometeorological station. The data were separated according to days with and without rain, and the analyses of correlation and of simple and multiple regressions were carried out, in order to obtain models with equations for leaf wetness estimation. Most of the equations that did not use the data of the sensors installed at 1.7 m, especially those of the models based on variables only from the agrometeorological station, presented low reliability. The models obtained with meteorological data only from the soybean crop show high reliability and use a lower amount of variables, which makes them a good alternative for wetness estimation.

Machine Learning Based Soft Sensing Tool for the Prediction of Leaf Wetness Duration in Precision Agriculture

“…Finally, source code and result logs are available at https://git.code.tecnalia.com/afarcloud/dss algorithms/leaves wetness. 3…”

“…In this context, the lack of a standard method for calculating and measuring the Leaf Wetness Duration (LWD) is arguably the most concerning constraint for achieving rigorous data [3]. This causes many practical difficulties when comparing results, and may lead to bad decisions made by growers about the diseases related to the leaf wetness.…”

Approaches for the Prediction of Leaf Wetness Duration with Machine Learning

“…(b) There are different types of sensors, and the measurements vary according to type [7]; thus, two sensors of different types in the same place could generate different results. (c) There is no standard protocol for installation of sensors and measurement of LWD, and even the specifications change according to sensor brand [12]. Sensor used by ICAFE and sensor from another brand.…”