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

Abstract

The tomato leafminer Tuta absoluta (Meyrick) is one of the most harmful pests of solanaceous crops. Its larval morphological characteristics are similar, making the distinguishing between different larval instars difficult. Accurate identification of T. absoluta instars is necessary either for population outbreak forecasting, or developing successful control programs. Although a clustering algorithm can be used to determine the number of larval instars, little is known regarding the use of density-based ordering points to identify the clustering structure (OPTICS) and determine the number of larvae. In this study, larval instars of 240 T. absoluta individuals were determined by the density-based OPTICS clustering method, based on mandible width, and head capsule width and length. To verify the feasibility of the OPTICS clustering method, we compared it with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, Gaussian mixture models, and k-means. Additionally, the instars determined by the clustering methods were verified using the Brooks–Dyar rule, Crosby rule, and linear regression model. The instars determined by the OPTICS clustering method were equal to those determined by the other types of clustering algorithms, and the instar results were consistent with the Brooks–Dyar rule, Crosby rule, frequency analysis, and logarithmic regression model. These results indicated that the OPTICS clustering method is robust for determining insect larva instar phase. Moreover, it was found that three morphological indices of T. absoluta can be used for determining instars of this pest in the field, which may provide important information for the management of T. absoluta populations.

Analysis of Antioxidant Capacity Variation among Thai Holy Basil Cultivars (Ocimum tenuiflorum L.) Using Density-Based Clustering Algorithm

“…OPTICS retains the same parameters as DBSCAN but makes the ε parameter optional for runtime complexity reduction. This algorithm is known for its versatility in generating high-quality clusters and its ability to classify noises in varying-density data [29,33]. To describe OPTICS, we introduced two additional parameters; namely, the core distance and reachability distance.…”

“…Nevertheless, the nonexplicit outputs require further analysis procedures to enable practitioners to determine the cluster patterns [31]. OPTICS has been utilized to cluster the integration of omics data, such as disease features (e.g., genes, proteins, pathways, and variants) [32], larval instars [33], wheat genotypic data [34], and metabolic features [35].…”

Instar determination by constrained gaussian mixture models according to Dyar’s rule

“…However, for many species, the change in body size for successive two instar stages is often ambiguous or involves an intraspecific variation, so determining their instars is challenging. Accordingly, various clustering methods have been suggested to determine the nymphal developmental stages of an insect based on the size of morphological characters (Logan et al 1998, Wu et al 2013, Merville et al 2014, Cen et al 2018, Yang et al 2018, Nguyen et al 2022, Wang et al 2022).…”

“…The gaussian mixture models can be used to determine the consistency of Dyar’s rule for an insect species with statistical hypothesis testing. That is important because while some studies suggest Dyar’s rule does not adapt to explain nymphal growth for some insect species (Peterson et al 2019, Nguyen et al 2022), many studies rely on a simple correlation coefficient between clustered labels and data or ratios between Dyar constants in subsequent instars (Merville et al 2014, Sukovata 2019, Nguyen et al 2022, Wang et al 2022). These approaches may provide some information about instar stages but do not provide statistically powerful tests to make a decision.…”