Published on April 2020 | Image Recognition

A Comprehensive Survey On Vision-Based Insect Species Identification and Classification

The research on automation of identification and classification, supported by studies of insect ecology and related domains was marked by the introduction of DAISY - digital automated identification system. The framework embarked multiple approaches to solve the problem, varying from PCA to NNC (nearest neighbour clustering) algorithms. Efforts involving statistical methods are also evident in the domain. Evolution of artificial neural networks, associative memory networks also marked major changes in the direction of effort towards the problem statement. Other machine-learning based classification techniques such as SVM, PCALC have also been used as solution methods. Clustering techniques combined with image features were revived with Correspondence filters. Various transforms such as Fourier, SIFT and wavelet as features also based some studies. With the dawn of deep learning, more advanced techniques such as pose estimation have also become a base to solution framing. Image-based techniques, involving pattern extraction have prevailed with exceptional results. Approaches towards automation of the process to the solution have been decorated ever since.