Automatic identification of insects from digital images: A survey

The monitoring of pests in the field or lab experiments allows to identify the variation of infection levels and enhance the development of integrated pest management programs. The use of traps to capture insects is alternative in different crops and regions. However, identification and manual counting of captured specimens often time-consuming, requires taxonomic knowledge, and relies on the expertise of specialists. Therefore, the automation of this process could reduce cost, increase accuracy, and scalable the analysis. Current computer vision and artificial intelligence techniques can identify objects of interest in digital images in a timely and accurate manner. Hence, this paper presents a survey considering the following Computer Science digital search databases: ACM, IEEE, IET, DBLP, ScienceDirect, Scopus, SpringerLink, and Web of Science. We found three hundred studies, published between 2015 to 2019, of which thirty-three were selected based on the eligibility criteria. Results showed the use of convolutional neural network approaches, techniques to improve feature extraction, the lack of treatment to insect overlapping, and the non-use of instance segmentation via deep learning.