Resumen
A sufficiently early and accurate prediction can help to steer crop yields more consciously, resulting in food security, especially with an expanding world population. Additionally, prediction related to the possibility of reducing agricultural chemistry is very important in an era of climate change. This study analyzes the performance of pea (Pisum sativum L.) seed yield prediction by a linear (MLR) and non-linear (ANN) model. The study used meteorological, agronomic and phytophysical data from 2016?2020. The neural model (N2) generated highly accurate predictions of pea seed yield?the correlation coefficient was 0.936, and the RMS and MAPE errors were 0.443 and 7.976, respectively. The model significantly outperformed the multiple linear regression model (RS2), which had an RMS error of 6.401 and an MAPE error of 148.585. The sensitivity analysis carried out for the neural network showed that the characteristics with the greatest influence on the yield of pea seeds were the date of onset of maturity, the date of harvest, the total amount of rainfall and the mean air temperature.