Insects are the largest, most diverse organism class. Their key role in many ecosystems means that it is important they are identified correctly for effective management. However, insect species identification is challenging and labour-intensive. This has prompted increasing interest in image-based systems for rapid, reliable identification supported by advances in deep learning, computer vision, and sensing technologies. We conducted a systematic literature review (SLR) to analyse and compare primary studies of image-based insect detection and species classification methods. We initially identified 980 studies published between 2010–2020 and selected from these 69 relevant studies using explicitly defined inclusion/exclusion criteria. In this SLR, we conducted a detailed analysis of the primary studies’ dataset properties (i.e. insect species targeted, crops, geographical locations, image capture methods) and insect classification techniques. We provide recommendations for future research based on the gaps our survey identified. We found many studies were conducted in China, the USA, and Brazil, but none in the African continent. The majority of the studies (78.3%) aimed to identify crop pests, mainly of rice and wheat. Only three studies specifically targeted beneficial insects, bee species and predatory species. Insect species targeted by the studies were centred around 10 insect orders out of 28. The analysis of classification methods shows a recent trend toward applying deep learning techniques compared to shallow learning techniques for insect identification. The SLR provides insight into the current state of the art and indicates promising future directions for image-based insect identification and species classification relevant to Computer Science, Agriculture and Ecology research.
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