## 4 Restrictions on Selected Input Variables  
Each independent variable used in predictive models, such as measurements of air temperature, relative air humidity, sum of precipitation, or soil physicochemical properties, may be limited due to human error or failure of the measuring devices. Errors in remote sensing data are often caused by weather and ground conditions (e.g., overcast and snow cover) and/or sensor problems (e.g., sensor drift and changes in sensor view angle). These errors may result in irregularly low data values [150]. The accuracy of measurements is important for ensuring reliable results [151]. Accuracy is in line with the term uncertainty, which covers a wider range of doubts or inconsistencies in the obtained data [152]. One source of uncertainty may be the subjectivity of data collection [153]. Limiting the occurrence of these conditions has important impacts on the accuracy of the model. Detecting data inaccuracies, also referred to as fraud detection [154], can contribute to increasing yield prediction accuracy. The identification of such data allows them to be ignored in modeling, which limits the amount of information that the neural network has to process. This may, in turn, shorten the time needed to generate a forecast and reduce the data that lower the model's accuracy. [PERSON] et al. [3] used an unmanned aerial vehicle to obtain NDVI and RGB data from fields located in the southwestern part of Philadelphia, USA, which were used to evaluate which of these indices produce better results in wheat and barley yield prediction using convolutional neural networks (CNNs). The results showed that using the RGB image predictor early in the growth phase (\(<\)25% of the total thermal time) produced a better functioning model compared with the NDVI model. The RGB image model was characterized by a mean absolute error of 0.48 t\(\cdot\)ha\({}^{-1}\) (MAPE was approx. 8.8%). The authors suggested that using deep network learning to forecast yield from drone photos may be useful as long as these images are taken relatively early in the season [3]. The NDVI does not have a feedback loop (open loop structure), which makes it susceptible to numerous errors and uncertainty given changing weather conditions and the background of the canopy. However, this problem was solved by the development of a modified NDVI indicator in the form of the EVI. This parameter is characterized by a higher resistance to canopy background noise and weather conditions [155; 156]. The higher efficiency of EVI in characterizing plant productivity makes it a more effective indicator in crop forecasting in comparison with NDVI. This is confirmed by [PERSON] and [PERSON] [157], who estimated the yield of maize and soybeans in central United States. Although EVI is able to reduce the noise associated with the influence of the atmosphere and the background of the canopy, it does not include topographic effects, which are defined as \"the change in radiation accompanying the change of orientation from a horizontal into an inclined surface in response to a change in the position of the light source\" [158]. The following effect is another important environmental factor influencing the formation of noise in the calibration of vegetation indices, which is of particular importance in mountainous areas [155]. Interestingly, [PERSON] et al. [9] demonstrated that in forecasting the yield of barley, wheat,and rapeseed, the NDVI was more accurate than the EVI. The NDVI appeared to be a more efficient vegetation index in forecasting crops in the Canadian praneural networks and the method of forecasting the grain yield of rice has crucial effects on the accuracy of prediction. The study considered fields located in the northern, central and southern parts of Burkina Faso. Three different forecasting methods were used: ANN, conventional multiple regression, and boosted regression trees.