Improved Random Forest for the Automatic Identification of Spodoptera frugiperda Larval Instar Stages

Abstract

Spodoptera frugiperda (fall armyworm, FAW) is a global agriculture pest. Adults have a strong migratory ability and larvae feed on the host stalks, which pose a serious threat for maize and other crops. Identification and counting of different instar larvae in the fields is important for effective pest management and forecasting emergence and migration time of adults. Usually, the technicians identify the larval instars according to the larva morphological features with the naked eye or stereoscope in the lab. The manual identification method is complex, professional and inefficient. In order to intelligently, quickly and accurately identify the larval instar, we design a portable image acquisition device using a mobile phone with a macro lens and collect 1st-6th instar larval images. The YOLOv4 detection method and improved MRES-UNet++ segmentation methods are used to locate the larvae and segment the background. The larval length and head capsule width are automatically measured by some graphics algorithms, and the larval image features are extracted by SIFT descriptors. The random forest model improved by Boruta feature selection and grid search method is used to identify the larval instars of FAWs. The test results show that high-definition images can be easily collected by using the portable device (Shenzhen, China). The MRES-UNet++ segmentation method can accurately segment the larvae from the background. The average measurement error of the head capsule width and body length of moth larvae is less than 5%, and the overall identification accuracy of 1st–6th instar larvae reached 92.22%. Our method provides a convenient, intelligent and accurate tool for technicians to identify the larval instars of FAWs.