Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Abstract

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

“…As a result of the learning process, and after conducting the validation, it was seen that the selected models were characterized by the lowest error value of RMS as well as a low value of MAPE, oscillating in the region of about 20%. The results (Table 3), in which RMSE and MAPE reached a value of about 10%, mean that the model performs well in the process of dried strawberry fruit identification [45,48].…”

“…MSE is probably the most commonly used error metric [44,45]; it penalizes larger errors, because squaring larger numbers has a greater impact than squaring smaller numbers. MAD is the sum of absolute differences between the actual value and the forecast divided by the number of observations [46][47][48].…”

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

“…The literature survey and our experience obtained during the creation of neural models allowed to identify several characteristic stages occurring during this process [20,25,37,[39][40][41][42]44,45,52]. The creation of an ANN model proceeds in five stages:…”

“…Nonlinear models, especially ANNs, are also increasingly used in agriculture, often obtaining better analysis results than classical statistical methods [44].…”

“…The ANNs with MLP topology to forecast winter rapeseed yields and winter wheat yield were developed. Using ANNs makes it possible to obtain an accurate yield forecast before harvesting (all tested models were characterized by high forecast accuracy) [39,44]. ANNs are also employed to control and predict variables in agricultural drying processes.…”