An expert system for insect pest population dynamics prediction

Abstract

Avocado (Persea americana) production is increasing in Kenya, with both small and largeholder farming for domestic and export markets. However, one of main challenges that limit production is infestation by insect pests, notably the oriential fruit fly Bactocera dorsalis and Ceratitis spp. fruit flies, which cause direct crop losses and are indirectly responsible for non-tariff trade barriers due to stringent export requirements. Data on weekly pest trap counts were collected between September 2017 and December 2020 within orchards in avocado plantations. Fuzzy neural network (FNN) were used to model the population dynamics of B. dorsalis and Ceratitis spp. Weekly pest counts, rainfall, average temperature, relative humidity and avocado plant physiological stages were used for predictive modeling in different orchards. The performance of the resulting models was evaluated using coefficient of determination (R2), mean absolute error (MAE), mean relative approximation error (MRAE) and root mean squared error (RMSE). FNN models achieved satisfactory results in predicting the dynamics of the pests in the orchards, with most of the models obtaining R2 > 0.85. We demonstrated how FNN models can be used as predictive tools for managing and controlling fruit fly pest populations in these plantations, and how they may be suitable to predict fruit fly or other pests in similar cropping systems. Once the input variables are known, they can be loaded into the FNN models to predict field pest populations, and based on threshold values, allow for implementation of timely and adequate control measures such as the use of biopesticides