Field scale wheat yield prediction using ensemble machine learning techniques

Wheat is crucial for global food security and plays significant role in achieving United Nations Sustainable Development Goal 2 (Zero Hunger). In India, wheat accounts for 33.5 % of total cereal production. Accurate and cost effective yield predictions are essential for maintaining food security. Wheat yield forecasting is influenced by various factors, such as genotype and environmental conditions. Among these, the effect of morpho-physiological traits in the field is important for predicting yield but hasn't been studied much using ensemble machine learning methods. This study aims to bridge this gap by evaluating 29 morpho-physiological traits to predict site specific wheat yield. Big data framework was used to develop and refine several ensemble machine learning models based on field trial datasets. The developed models are optimized to reduce errors and prevent overfitting and underfitting, boosting their predictive precision. Each model's efficiency was evaluated using performance metrics such as mean absolute error, mean absolute deviation, root mean square error, R², and overall accuracy. The ensemble model, which combines random forest (RF) and artificial neural networks (ANN), demonstrated better performance by achieving a mean absolute percentage error of 4.65 %, and R² value of 98.48 % and 98.18 % accuracy on test data.Our results demonstrate that ensemble models combining RF with support vector regression (SVR) outperformed individual models such as RF, SVR, ANN, multivariate adaptive regression splines (MARS). These findings are a promising step towards future research focused on creating more advanced ensemble methods with finely tuned hyperparameters for improving the accuracy of large-scale wheat yield prediction.