Abstract
One of the most important applications of remote imaging systems in agriculture, with the greatest impact on global sustainability, is the determination of optimal crop irrigation. The methodology proposed by the Food and Agriculture Organization (FAO) is based on estimating crop evapotranspiration (ETc), which is done by computing the reference crop evapotranspiration (ETo) multiplied by a crop coefficient (Kc). Some previous works proposed methods to compute Kc using remote crop images. The present research aims at complementing these systems, estimating ETo with the use of soil moisture sensors. A crop of kikuyu grass (Pennisetum clandestinum) was used as the reference crop. Four frequency-domain reflectometry sensors were installed, gathering moisture information during the study period from May 2015 to September 2016. Different machine learning regression algorithms were analyzed for the estimation of ETo using moisture and climatic data. The values were compared with respect to the ETo computed in an agroclimatic station using the Penman–Monteith method. The best method was the randomizable filtered classifier technique, based on the K* algorithm. This model achieved a correlation coefficient, R, of 0.9936, with a root-mean-squared error of 0.183 mm/day and 6.52% mean relative error; the second-best model used artificial neural networks, with an R of 0.9470 and 11% relative error. Thus, this new methodology allows obtaining accurate and cost-efficient prediction models for ETo, as well as for the water balance of the crops.
Highlights
In agricultural sciences, the optimal determination of crop water needs over time is based on measuring the soil water balance and the evaporative demand of the plants
The ultimate goal of the present research is to predict the actual values of the crop are shown in Figure 4 for a crop of lettuce (Lactuca sativa L) which was used as the crop of interest, evapotranspiration, ETc, which is a part of the water balance equation [37]
In the case of the regression trees (RT), the algorithm used for construction of the decision trees was Classification and Regression Trees (CART) [43]
Summary
The optimal determination of crop water needs over time is based on measuring the soil water balance and the evaporative demand of the plants. Sci. 2020, 10, 1912 for computing this balance were developed by different authors [1,2,3] These techniques were applied in agriculture to obtain the water needs in conjunction with other methods based on remote image sensing. There is a wide range of techniques for measuring soil moisture based on electricity, which are applied in geophysical prospecting [7,8] and agronomy [9,10], among other areas. In these measuring techniques, capacitive methods such as frequency-domain reflectometry (FDR) are included [11,12,13]. The accuracy of such sensors varies due to the employed techniques and working conditions
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