Automatic identification of insects from digital images: A survey.

• A systematic review on the applicability of machine learning and image processing in pest management. • Review of 33 relevant research studies. • The use of traps, image datasets, or plant images were the most used techniques. • Great opportunity to exploit the insect overlapping problem, customizations for small objects, and instance segmentation. The monitoring of pests in the field or lab experiments allows to identify the variation of infection levels and to enhance the development of integrated pest management programs. The use of traps to capture insects is an alternative in different crops and regions. However, identification and manual counting of captured specimens is 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 research 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. [ABSTRACT FROM AUTHOR]