Abstract
Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.
Highlights
Maximizing yield while minimizing costs and caring for the environment are the basic goals of agricultural production [1]
Forecasting the yield of the same plant species, but grown in a different field, may result in obtaining different weights for the same independent variables in the process of training the neural network as other key variables affect the yield. All these aspects vary the crop yield result depending on time and space, as evidenced by the research conducted by Adisa et al [114], who analyzed the alternations in agroclimatic parameters affecting maize productivity in the north-eastern part of South Africa
The analysis indicated that satellite data can be successfully used to determine parameters such as leaf area index (LAI), canopy chlorophyll content index (CCCI), and weighted difference vegetation index (WDVI)
Summary
Maximizing yield while minimizing costs and caring for the environment are the basic goals of agricultural production [1]. RMSE indicates the absolute fit of the observed data point to the predicted values It is determined, according to Formula (2), as the root of the second order from the mean square of all errors [26,30]. The application of artificial neural networks (ANNs) in agriculture solved the problem of the lack of linearity between the crop yield and independent variables. The results showed that neural networks outperformed other techniques in terms of grain yield prediction quality, with R2 values ranging from 0.31 to 0.74. The author obtained a model that allowed forecasting the yield on June 30, having the lowest value of the mean absolute percentage error of 9.43%.The neural model research studies by Ayoubi and Sahrawat [70]. Such information is crucial as it indicates the condition of the vegetation, which is sensitive to both abiotic and biotic factors [74]
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