Instar Determination of Blaptostethus pallescens (Hemiptera: Anthocoridae) Using Artificial Neural Networks

Abstract

Abstract Blaptostethus pallescens Poppius is an important predator of vegetable pests in tropical regions. The correct identification of the stages of the life cycle of predatory species is crucial, since different stages may present different rates of pest consumption. Artificial neural networks (ANNs) are computational tools with a structure based on the human brain. With applications in several fields, ANNs have been applied in pest management for identification of pest species, spatial distribution modeling, and insect forecasting. The objective of this study was to apply ANNs as a method for the instar determination of B. pallescens using three morphometric measures (head width, body width, and body length). Cluster analysis was performed to categorize the insects in instars according to the morphometric variables. Subsequently, the ANNs were trained for instar determination using the morphometric measures as input variables. The ANNs tested (with 2, 4, 6, 8, 10, and 12 hidden neurons) provided proper data fitting (R2 > 98%). However, due to the parsimony principle, the network with hidden layer size 6 was selected. This study shows the successful application of ANNs in the instar determination of B. pallescens, which would not be possible using classical methods.