Abstract
Since ancient times, entomology has been firmly ingrained in many societies for agricultural
purposes. In order to preserve the variety of species within our environment, many scientists
nowadays are interested in the topic of biodiversity. More than two thirds of the 1.3 million
species that are known to exist on Earth are insects. Humans and insects have interacted in a
variety of ways since 400 million years ago. There have been several efforts to develop a reliable
technique for identifying insects. Accurate insect identification requires extensive entomology
knowledge and expertise. The lack of qualified entomologists necessitates the automation of
insect identification. We provide a framework for automatically identifying insects that can
recognize butterflies and grasshoppers from colorful photos. For a proof-of-concept, two bug
classes are selected. Since each class of sample cases has a different color and unique body form,
classification is accomplished by changing the color and shape features of insects. The first step
in the suggested insect identification procedure is to extract characteristics from samples and
divide them into two training sets. While one training focuses on calculating RGB characteristics,
the other is normalized to estimate the binary color area that represents the insect's form. The
collected data is trained using an SVM classifier. The classifier's final conclusion determines
which class an unknown instance belongs to by combining the results of these two attributes. The
first findings show how effective and efficient our two-step automated insect identification
method is, and they encourage us to expand this framework to recognize a wide range of
additional insect species.