Abstract
In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.
Highlights
The current undesirable effects of agriculture on the biosphere cannot be reduced using traditional experiments (Longchamps et al, 2018)
Spatial smoothing was tested in the case of continuous maize yield prediction
The aim of this study Querywas to compare different models and to develop a model that predicts maize yield according to a spatio-temporal database
Summary
The current undesirable effects of agriculture (greenhouse gas emission, groundwater pollution or nitrification) on the biosphere cannot be reduced using traditional experiments (Longchamps et al, 2018). Farm and in-situ observations together with existing databases provide the opportunity to predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, but they enable the potential for machine learning (ML) to be used The latter has the advantage of being able to handle many parameters indefinitely in time and space, i.e., big data databases created using precision management tools and data collection capabilities can be used mostly in the area of meteorological, technological or soilrelated information, including characteristics of different plant species. One viable solution for this problem is site-specific data collection, i.e., obtaining as much information about events as possible during the vegetation period, as it is possible to determine the processes and their causes, and to find solutions to prevent negative effects on the environment after precise intervention This approach will result in a controlled, monitored environment in space and time. There has to be a change in scientific methods and the potential of big data needs to be better exploited than before, which will result in the need to use artificial intelligence (AI), ML methods
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