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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/340628346 A Comprehensive Survey On Vision-Based Insect Species Identification and Classification Preprint · April 2020 DOI: 10.13140/RG.2.2.10083.50720 CITATIONS READS 0 221 3 authors: Chinmaya Hs Manoj Balaji Jagadeeshan Cambridge Institute of Technology Rakuten 1 PUBLICATION 0 CITATIONS 3 PUBLICATIONS 0 CITATIONS SEE PROFILE SEE PROFILE Ganesh N Sharma University Visvesvaraya College of Engineering 3 PUBLICATIONS 0 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Insect Detection View project Handwritten Character Recognition View project All content following this page was uploaded by Manoj Balaji Jagadeeshan on 21 April 2020. The user has requested enhancement of the downloaded file. A Comprehensive Survey On Vision-Based Insect Species Identification and Classification H S, Chinmaya * J, Manoj Balaji * Sharma, Ganesh N * Abstract Insects in ecology: Insects are morphologically identi- fiable with soft body texture and are found to flaunt various The research on automation of identification and clas- defence mechanisms such as camouflage, secretions to sification, supported by studies of insect ecology and re- counter the predetors, often in vain due to the difference lated domains was marked by the introduction of DAISY in size compared to other organisms. Insects being the - digital automated identification system. The framework primal in the list of pollinators, most of the flowering plants embarked multiple approaches to solve the problem, vary- depend on them for reproduction, while some variants for ing from PCA to NNC (nearest neighbour clustering) algo- source of nutrition as in the case of insectivourous plants. rithms. Efforts involving statistical methods are also evident in the domain. Evolution of artificial neural networks, asso- ciative memory networks also marked major changes in the Interaction with humans: A prominent group of direction of effort towards the problem statement. Other insects, which are called pests, can be characterized by machine-learning based classification techniques such as having harmful or destructive effects on their environment. SVM, PCALC have also been used as solution methods. They contribute to unintended outcomes through various Clustering techniques combined with image features were means such as the spread of diseases, crop destruction. revived with Correspondence filters. Various transforms Some of the most commonly found illustrations include such as Fourier, SIFT and wavelet as features also based a wide variety of mosquito, common fly and caterpillar some studies. With the dawn of deep learning, more ad- species. On the other hand, some species of insects, such vanced techniques such as pose estimation have also be- as dragonflies and mantises act as predators to many others come a base to solution framing. Image-based techniques, of their own, thus lending a helping hand to agriculturists. involving pattern extraction have prevailed with exceptional A wide varietal range of species of insects is consumed results. Approaches towards automation of the process to as food all over the globe, illustrated by gross hoppers the solution have been decorated ever since. and ants. Multiple technological inspirations have been drawn by insects and their behavioural mechanisms, as in, compound eyes, and hi-resolution cameras. 1. Introduction ”Insects” is the colloquial term for Hexapods; biologi- cally classified into Orthopods. These creatures appear in Owing to their populous nature, primal tasks in studies various sizes, shapes and colours; often intriguing physical around insects are identification and classification. While appearances. In the biological category, they are members identification involves diving into the details and features of kingdom Animalia, phylum Arthro- poda, and class that uniquely define them, classification involves gather- Insecta, They contribute 90 per cent of the animal life forms ing commonality across the groups formed of similar ones. in the world and is estimated that there are around 5.5 This literature exploration involves the investigation of 22 million different insect species on earth. Their impact, even manuscripts published from 1996 to 2019, chronologically. though most of them are relatively smaller in size to the The research in this field gained importance during the grate creatures around them, on the environment is as widespread crisis but eventually, the hype went down with time. Even as their presence too. With this large distribution, they play though this survey hints of summarisations of topics across a major role in human life and the ecosystem. 80% of the various related domains, our major focus is on the image- countries in the world consume insects as food. They play based identification and classification efforts which numer- a major role in pollination and hence contributing to plant ous technical aspects and procedures. A detailed discus- reproduction. sion to provide worthy insights and in-depth understanding, about the limitations and improvements is thus unfolded. 1 2. Insect identification and classification transforms from a pattern space of higher-dimensions to a low-dimensionality component space each time material 2.1. Classical Methods is added or removed from the system, PCA for DAISY Insects are one of the prominent members of Animalia resulted inadequate for the task. As an improvement, the kingdom. They belong to Phylum Arthropoda and Class introduction of nearest neighbour classifiers for DAISY Insecta following the Linnaean taxonomy. The process of were welcomed. The robustness and simplistic nature of identification revolves around the parameters used in the the technique assisted in wide-spread applications ranging categorisation. Any species that belong to these categories from insect identification to plant leaf and human face are later classified into a different order, family, genus, and identification. Although, DAISY as a pattern recognition species. Often, the family name is enough for identification engine is limited by background noises, pose scale, cluster but manually, an expertise in understanding their juvenile geometry in pattern space. stage, adult stage, and immature stage becomes a must. A deeper understanding of insect characteristics such as More focused efforts on particular species identification 3 pairs of legs, three body regions namely, head, body also bloomed. P.J.D. Weeks et al outlined in their work[12], and thorax, zero, one and two pairs of wings and a pair a semi-automated technique, to identify the individuals be- of antenna, as highlighted in [9], plays a key role in the longing to 5 closely related species based on the difference process too. To conclude, an in-depth understanding and in wing patterns. The system is able to extract the informa- knowledge regarding the morphology and life cyle of tion and consider the variation in individuals of the same instects are requried to orchestrate a simplest effort of species, morphological features to identify individuals, identification or classification. the key differences between patterns of different species. The model works in different modules among these There are 26 orders belonging to the Class Insecta Gaussian smoothed images give higher accuracy. Gaussian of which some of them require profound efforts to be smoothing highlights the features better than others. Hence identified [9], due to their size and habitat. Experienced making it successful than previous work. The same authors taxonomists use features such as habitat, food habit, shape, proposed a newer technique involving associative memory body armor structure, wing patterns if any, size of the an- networks to identify wasp species based on wing pattern tenna, larval stage behavior, reproduction, and other visual and venation in [13]. Using Kendal’s T, a statistical aspects to classify them and is undoubtedly a challenging method, the authors classified the insect and found out that task. accuracy is affected by number of images given for training. Techniques apart from, classical observation, used to Contemporarily, improvementative reseach on DAISY identify the insects are mentioned in [19] and [7]. The still yeilded a more evolved technique[14], in which digital authors highlight DNA based techniques and internal mor- automated identification system, first digitizes the image phology. While, the DNA based techniques implement fin- and converts it to a vector, which is then subjected to gerprinting technology to identify and classify the insect principal component analysis. The PCA gives a set of species procedures pertaining to internal morphology in- eigenfunctions that describe the variations accurately. The volves, dissecting the insect and later study the internal parts variations may be rotation and scaling. Then, Kendal’s T to identify and understand the food habits and other related statistics, is used to compare the original and reconstructed information. images. P.J. D.Weeks et al. outlined the benchmarks to explore the limitations of the prototype system. 60% of the 2.2. Visual Identification dataset was classified as the training set and other 30% for Identification of different organisms involves the first the testing set. The classifier with the highest correlation step being towards understanding the behaviour and other will be considered correct. The highest accuracy was

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