Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield

Abstract

The aim of the research was to create a prediction model for winter rapeseed yield. The constructed model enabled to perform simulation on 30 June, in the current year, immediately before harvesting. An artificial neural network with multilayer perceptron (MLP) topology was used to build the predictive model. The model was created on the basis of meteorological data (air temperature and atmospheric precipitation) and mineral fertilization data. The data were collected in the period 2008-2017 from 291 productive fields located in Poland, in the southern part of the Opole region. The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics. An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data. The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure, which was 9.43%. The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape. The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017 (designation by the T1-4_CY model).

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

“…In particular, tools based on artificial intelligence-artificial neural networks (ANN), giving significantly lower prediction errors than in case of statistical methods-are very popular. Therefore, crop yield models are implemented in computer applications for precision agriculture and are becoming an important element of decision support systems [9][10][11][12][13][14][15][16][17][18].…”

“…The use of combined data (quantitative and qualitative) may in consequence give a better result than in case of only one type of data (only quantitative or only qualitative). Therefore, the authors of this work, encouraged by previous research results [11,19,20], have undertaken the construction and analysis of winter rapeseed yield models based on quantitative and qualitative data. The produced models can be used to simulate the crop in the current agrotechnical season before harvesting.…”

“…In the case of cultivating several varieties on a dozen or several dozen areas of fields simultaneously, very accurate monitoring of development phases causes problems. Therefore, in many works, the authors of the models use specific dates of the calendar year, which correspond to key developmental phases in typical years [10,11,20].…”

Using artificial neural network in determining postharvest LIFE of kiwifruit

“…The kiwifruit yield can be influenced by different factors, particularly soil fertilization and plant nutrition . Niedbała used fertilization data for the development of an ANN model to predict the yield of winter rapeseed. One of the most important relationships is the dependency of yield and nutrients status in the plant.…”

Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

“…These time series allow yields to be estimated and production mapped in most cases at the end of the agricultural season and at spatial scales ranging from the agricultural region (by using medium-resolution images to cover areas of several tens of km 2 ) to the plot scale (by averaging information acquired at high spatial resolution over several hectares) [8][9][10][11][12]. The methods used are then diverse, ranging from simple empirical relationships (based on vegetation indices derived from reflectance [13][14][15]) to the assimilation of biophysical parameters (such as the leaf area index or the fraction of absorbed photosynthetically active radiation, derived from optical images by inversion of a radiative transfer model) in agro-meteorological models [16][17][18], or the use of different statistical algorithms (e.g., artificial neural network, partial least squares regression, support vector machine or random forest [19][20][21][22]). The latter approach has the advantage of obtaining high performance, particularly in a multi-factorial context, but it is conditioned by the availability of data (particularly access to field truths), which often constrains the possibilities of implementation (i.e., difficulty of independent calibration and validation procedures) and limits the representativeness of the algorithms.…”