Integrating Climate Variable Data in Machine Learning Models for Predictive Analytics of Tomato Yields in California

Traditionally, agricultural forecasting has relied on empirical methods and basic statistical analysis, such as applying average values from previous years' yields or using a simple linear fit for next year's predictions. However, the emergence of data-driven approaches, particularly machine learning algorithms, has revolutionized yield prediction in agriculture. Machine learning techniques have demonstrated their potential to provide accurate predictions. However, existing models often rely on a limited number of input variables for crop yield predictions, which makes them only suitable for specific scenarios. In this study, we have developed four distinct machine learning-based predictors, incorporating various climate factors, including daytime temperature, nighttime temperature, precipitation (rainfall), vegetation index, and evapotranspiration as input variables to predict tomato acreage yields in counties of California, USA. Our results show that regression models constructed using neural networks and linear regression exhibited better performance than other predictors, achieving an average accuracy rate of 70% to 80%. Compared to most of the existing crop yield predictors, our models offer versatility while maintaining a desirable level of predictive accuracy. Expanding the number of input variables, such as nitrogen fertilizer usage etc, and introducing larger spatial and temporal high-resolution datasets for model training can improve our model performance, enabling us to obtain better results in tomato yield prediction.