Modelling and Forecasting of Greenhouse Whitefly Incidence Using Time-Series and ARIMAX Analysis

Abstract

Greenhouse whitefly (Trialeurodes vaporariorum) is a major insect pest of greenhouse crops. To prevent the damage caused by whiteflies, farmers control the population of whiteflies by spraying pesticides in a regular basis. However, pesticides are costly and may affect the environment and health of farmers. To provide a more efficient way for applying pesticides, this research aims to develop a model for predicting the possible increase in whitefly population in greenhouses using autoregressive integrated moving average (ARIMA) and ARIMA with exogenous variables (ARIMAX). The data used in this work were collected using wireless imaging devices that can monitor the number of whiteflies trapped on sticky paper traps using an automatic insect counting algorithm. The wireless imaging devices were installed in a greenhouse that grew tomato seedlings, which is one of the host plants of whiteflies. The ARIMA and ARIMAX models were compared by setting different combinations of input data. Particularly, ARIMA includes only the whitefly count while ARIMAX includes the whitefly count and environmental data. Based on preliminary testing, the minimum number of input data was found to be at around 60 days to 90 days. ARIMAX was found to be the best model with input data including the increase in whitefly counts, temperature and humidity. In average, the RMSE for 7-day forecasting of the proposed method was found to be around 1.30. To assist farmers in decision-making for pesticide application scheduling, four levels of increase in whitefly count were defined such as Normal, Moderate, High, and Critical, which were determined using K-means clustering algorithm, and testing results on a testing dataset show an F1-scores of 0.86 and 0.42 for Normal and Moderate levels of daily increase in whitefly count.