Instar Determination for the Tomato Leafminer Tuta absoluta (Lepidoptera: Gelechiidae) Using the Density-Based OPTICS...

Area of Science:

• Agricultural Entomology
• Computational Biology
• Pest Management

Background:

• The tomato leafminer, Tuta absoluta, poses a significant threat to solanaceous crops.
• Distinguishing between larval instars of T. absoluta is challenging due to similar morphological characteristics.
• Accurate instar identification is crucial for forecasting outbreaks and implementing effective control strategies.

Purpose of the Study:

• To evaluate the effectiveness of the Ordering Points To Identify Clustering Structure (OPTICS) algorithm for determining T. absoluta larval instars.
• To compare OPTICS with other clustering methods (DBSCAN, Gaussian mixture models, k-means) and established entomological rules (Brooks-Dyar, Crosby).
• To validate the use of specific morphological indices for field-based instar determination.

Main Methods:

• Collected morphological data (mandible width, head capsule width and length) from 240 T. absoluta larvae.
• Applied the OPTICS clustering method to classify larval instars.
• Compared OPTICS results with DBSCAN, Gaussian mixture models, k-means, Brooks-Dyar rule, Crosby rule, and regression models.

Main Results:

• The OPTICS clustering method successfully determined T. absoluta larval instars.
• Results from OPTICS were consistent with other clustering algorithms and established entomological rules.
• Three key morphological indices were identified as reliable indicators for field identification of T. absoluta instars.

Conclusions:

• The OPTICS clustering method is a robust and reliable tool for determining insect larval instars.
• Morphological indices of T. absoluta can be effectively used for field-based instar determination.
• This research provides valuable insights for managing T. absoluta populations and forecasting outbreaks.