Determining the minimum data size for the development of artificial neural network-based prediction models for rice pests in Korea

Abstract

Sudden outbreaks of crop pests (insect pests and diseases) are increasing in Korea due to climate change and globalization. To prevent such outbreaks, it is necessary to predict and control pest occurrences in advance. Crop pests have been predicted through process-based or statistical modeling; however, the limitations of these models, which rely solely on historically acquired domain knowledge and data, have become increasingly prominent owing to climate change and rapidly changing agricultural ecosystems. To overcome these limitations, artificial neural network (ANN)-based models that use continuous pest survey data in the field have been investigated over the last decade. However, because pest survey data are collected by humans through process-mediated methods, fundamental problems exist in terms of data quality and size that may hinder the performance of the resulting ANN-based models. In this study, to determine the minimum pest data size required to ensure the optimal performance of ANN-based models, we developed feed-forward neural network models for 19 rice pests using 23 pest datasets collected from 149 districts by the Rural Development Administration of Korea over 19 years (2002–2020). Using each ANN-based model, the minimum data size required for the highest model performance achieved in this study was determined for all 19 rice pests. Furthermore, we developed a decision-tree rule to estimate the minimum data size based on the selected characteristics of each pest. The final Decision tree rule, based on the distinction between diseases and insect pests and the balance of pest data (the relative ratio of pest occurrence data to non-occurrence data), showed a relatively good performance (70.24 %) in the 3-fold cross-validation test. Overall, these results indicate that the minimum data sizes required for ANN modeling vary among rice pests, depending on the pest data characteristics, as indicated by the Decision tree rule developed in this study.