No abstract

Analysis of landmark-based morphometric measurements taken on body parts of insects have been a useful taxonomic approach alongside DNA barcoding in insect identification. Statistical analysis of morphometrics have largely been dominated by traditional methods and approaches such as principal component analysis (PCA), canonical variate analysis (CVA) and discriminant analysis (DA). However, advancement in computing power creates a paradigm shift to apply modern tools such as machine learning. Herein, we assess the predictive performance of four machine learning classifiers; K-nearest neighbor (KNN), random forest (RF), support vector machine (the linear, polynomial and radial kernel SVMs) and artificial neural network (ANNs) on fruit fly morphometrics that were previously analysed using PCA and CVA. KNN and RF performed poorly with overall model accuracy lower than “no-information rate” (NIR) (p value > 0.1). The SVM models had a predictive accuracy of > 95%, significantly higher than NIR (p < 0.001), Kappa > 0.78 and area under curve (AUC) of the receiver operating characteristics was > 0.91; while ANN model had a predictive accuracy of 96%, significantly higher than NIR, Kappa of 0.83 and AUC was 0.98. Wing veins 2, 3, 8, 10, 14 and tibia length were of higher importance than other variables based on both SVM and ANN models. We conclude that SVM and ANN models could be used to discriminate fruit fly species based on wing vein and tibia length measurements or any other morphologically similar pest taxa. These algorithms could be used as candidates for developing an integrated and smart application software for insect discrimination and identification. Variable importance analysis results in this study would be useful for future studies for deciding what must be measured.

Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.