AI-Based Modeling of Leaf Miner Incidence in Tomato Crops at Rajahmundry, India

Abstract

This study investigates the population dynamics of the leaf miner (Liriomyza trifolii) in tomato (Solanum lycopersicum Linnaeus) crops over eight consecutive years (2011–2018) during the Kharif season, with a focus on the relationship between pest population and various weather parameters. The weather variables examined include maximum and minimum temperature (MaxT and MinT), morning and evening relative humidity (RHM and RHE), sunshine hours (SS), wind velocity (Wind), total rainfall (RF) and the number of rainy days (RD). The findings reveal that the highest average population of leaf miners (1.3 larvae per plant) was observed in the protected experimental field during the 31^st Standard Meteorological Week (SMW) of 2012. In contrast, the lowest population (0.1 larvae per plant) was recorded in the unprotected experimental field in 2016. Correlation analysis highlighted that wind velocity and rainy days (both current and lagged) exhibited both negative and positive influences, respectively, on leaf miner incidence. Additionally, minimum temperature and evening relative humidity negatively impacted leaf miner populations, while maximum temperature and rainy days (current and lagged) had a highly significant positive effect on pest growth. To develop predictive models for leaf miner incidence, the study applied various machine learning techniques, including support vector regression (SVR), random forest (RF), and traditional statistical models such as multiple linear regression (MLR), general regression neural network (GRNN), and feedforward neural network (FFNN). The performance of these models was compared based on root mean square error (RMSE) values. Among the models, the random forest (RF) model outperformed others by yielding the lowest RMSE values, indicating superior prediction accuracy. The Diebold-Mariano (D-M) test was further employed to assess the forecasting performance of the applied models, and the random forest model was found to provide the most accurate predictions of leaf miner incidence. The analysis was conducted using the R programming language. In conclusion, demonstrates that weather variables, particularly maximum temperature and rainy days, significantly affect leaf miner populations in tomato crops. The random forest model proved to be the most effective tool for predicting pest incidence, offering valuable insights for integrated pest management strategies in agriculture.