Prediction of Pea (Pisum sativum L.) Seeds Yield Using Artificial Neural Networks

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, related the possibility of reducing agricultural chemistry is very important era climate change. This study analyzes performance pea (Pisum sativum L.) seed yield by a linear (MLR) non-linear (ANN) model. The used meteorological, agronomic phytophysical data from 2016–2020. neural model (N2) generated highly predictions yield—the correlation coefficient was 0.936, RMS MAPE errors were 0.443 7.976, respectively. significantly outperformed multiple regression (RS2), which had error 6.401 148.585. sensitivity analysis carried out for network showed that characteristics greatest influence on seeds date onset maturity, harvest, total amount rainfall mean air temperature.