  Deep learning-based approaches have produced models with good insect
classification accuracy; Most of these models are conducive for application in
controlled environmental conditions. One of the primary emphasis of researchers
is to implement identification and classification models in the real
agriculture fields, which is challenging because input images that are wildly
out of the distribution (e.g., images like vehicles, animals, humans, or a
blurred image of an insect or insect class that is not yet trained on) can
produce an incorrect insect classification. Out-of-distribution (OOD) detection
algorithms provide an exciting avenue to overcome these challenge as it ensures
that a model abstains from making incorrect classification prediction of
non-insect and/or untrained insect class images. We generate and evaluate the
performance of state-of-the-art OOD algorithms on insect detection classifiers.
These algorithms represent a diversity of methods for addressing an OOD
problem. Specifically, we focus on extrusive algorithms, i.e., algorithms that
wrap around a well-trained classifier without the need for additional
co-training. We compared three OOD detection algorithms: (i) Maximum Softmax
Probability, which uses the softmax value as a confidence score, (ii)
Mahalanobis distance-based algorithm, which uses a generative classification
approach; and (iii) Energy-Based algorithm that maps the input data to a scalar
value, called energy. We performed an extensive series of evaluations of these
OOD algorithms across three performance axes: (a) \textit{Base model accuracy}:
How does the accuracy of the classifier impact OOD performance? (b) How does
the \textit{level of dissimilarity to the domain} impact OOD performance? and
(c) \textit{Data imbalance}: How sensitive is OOD performance to the imbalance
in per-class sample size?
