Image recognition-based deep learning model for identifying the developmental stages of Acyrthosiphon pisum (Hemiptera: Aphididae)

Abstract

The small size and extensive polymorphisms of aphids make it difficult to identify larvae and adults solely based on their morphology. Here, we present an identification tool for the developmental stages of Acyrthosiphon pisum (Hemiptera: Aphididae) based on deep learning as a proof of concept. You Only Look Once (YOLO) algorithm is one of the most effective deep learning techniques for object detection. Although several studies have been conducted using deep learning technology for the detection and counting of tiny pests, the type of light source and size of the images were the limiting factors, as training was highly focused on uniform datasets and small insects. One way to overcome this problem is to introduce many types of datasets obtained from various light sources and microscopic magnifications. This strategy minimizes errors and omissions in aphid detection across all developmental stages in aphid individuals to the greatest extent possible. The experimental results showed that our modified YOLOv8 model could obtain over 95.9% and 99% accuracy for mean average precision (mAP) and recall, respectively, under various light sources, such as yellow, white, and natural light, and stereomicroscope magnifications. This study showed an improved accuracy of aphid recognition at all developmental stages. The study presents a novel deep learning model utilizing the YOLO algorithm to identify developmental stages of A. pisum. This model achieves high accuracy across various light sources and magnifications, thereby enhancing aphid biology studies.