Akademik Veri Yönetim Sistemi
JOURNAL OF FIELD ROBOTICS, cilt.43, sa.2, ss.761-778, 2026 (SCI-Expanded)
Western Flower Thrips (Frankliniella occidentalis) is a
significant agricultural pest causing substantial economic losses by
damaging crops and acting as a vector for plant diseases. Traditional
pest control methods relying on chemical pesticides pose environmental
and health risks, necessitating alternative solutions. Entomopathogenic
nematodes (EPNs) have emerged as a promising biological control agent.
This study presents an AI-supported precision application system,
Nemabot, designed to optimize EPN deployment based on thrips-induced
bean leaf damage. In this study, agricultural disease detection was
performed using the Multi-Otsu Thresholding method integrated into deep
learning-based object detection and segmentation algorithms. The
developed method enhances segmentation accuracy through image processing
techniques, thereby increasing the precision in identifying infested
regions. The model used in the study was optimized with a YOLO-based
architecture during training and reinforced with various data
augmentation techniques for segmenting bean leaves. The model's
performance evaluation yielded mAP0.5 values of B: 0.9481 and M:
0.94981, while mAP0.5:0.95 values were B: 0.90887 and M: 0.90887. The
precision and recall values were 1.0 and 0.99975, respectively,
indicating the model's high sensitivity. Additionally, the low values of
box_loss, segmentation_loss, and objectness_loss demonstrate that the
model maintains a minimal error rate. The proposed approach offers
higher accuracy and sensitivity than conventional segmentation methods,
contributing significantly to agricultural disease detection
applications.