Detection of Soil-Borne Pathogens Using Fine-Tuned Deep Learning Models: A Case Study on the Soybean Cyst Nematode (Heterodera glycines Ichinohe)

Soybean (Glycine max [L.] Merr.) is a globally important crop; however, its productivity is severely constrained by the soybean cyst nematode (Heterodera glycines Ichinohe). This nematode often remains undetected during early infection and persists in the soil as dormant cysts, causing long-term yield losses. Although conventional detection methods, such as microscopic inspection and polymerase chain reaction assays, provide accuracy, they are labor-intensive and unsuitable for large-scale monitoring. Therefore, an artificial intelligence-based framework was established for the classification and segmentation of female soybean cyst nematodes using advanced deep learning architectures. Soil samples were collected from infected fields in South Korea and female nematodes were imaged with red-green-blue cameras under a dissecting microscope. Instance segmentation was benchmarked across YOLOv5, YOLOv8, YOLOv11, and Detectron2. The finetuned YOLOv11 model achieved the best performance, with a precision of 0.977, a recall of 0.980, and a mean Average Precision at 50% intersection-over-union of 0.988. Additionally, color-based phenotyping using hue-saturation-value thresholds classified 4,392 nematode images into yellow, orange, and brown groups, representing the reproductive and developmental stages. Consequently, this integrated framework highlights the potential of artificial intelligence-driven detection systems to reduce labor-intensive practices and support sustainable soybean production through the improved management of nematode-induced yield losses.