Entomopathogenic nematode detection and counting model developed based on A-star algorithm

Abstract

Entomopathogenic nematodes are soil-dwelling living organisms widely employed in the biological control of agricultural insect pests, serving as a significant alternative to pesticides. In laboratory procedures, the counting process remains the most common, labor-intensive, time-consuming, and approximate aspect of studies related to entomopathogenic nematodes. In this context, a novel method has been proposed for the detection and quantification of Steinernema feltiae isolate using computer vision on microscope images. The proposed method involves two primary algorithmic steps: framing and isolation. Compared to YOLO-V5m, YOLO-V7m, and YOLOV8m, the A-star-based developed network demonstrates significantly improved detection accuracy compared to other networks. The novel method is particularly effective in facilitating the detection of overlapping nematodes. The developed algorithm excludes processes that increase space and time complexity, such as the weight document, which contains the learned parameters of the deep learning model, model integration, and prediction time, resulting in more efficient operation. The results indicate the feasibility of the proposed method for detecting and counting entomopathogenic nematodes.