Migration is a key process in the population dynamics of numerous insect species, including many that are pests or vectors of disease. Identification of insect migrants is critically important to studies of insect migration. Radar is an effective means of monitoring nocturnal insect migrants. However, species identification of migrating insects is often unachievable with current radar technology. Special-purpose entomological radar can measure radar cross-sections (RCSs) from which the insect mass, wingbeat frequency and body length-to-width ratio (a measure of morphological form) can be estimated. These features may be valuable for species identification. This paper explores the identification of insect migrants based on the mass, wingbeat frequency and length-to-width ratio, and body length is also introduced to assess the benefit of adding another variable. A total of 23 species of migratory insects captured by a searchlight trap are used to develop a classification model based on decision-tree support vector machine method. The results reveal that the identification accuracy exceeds 80% for all species if the mass, wingbeat frequency and length-to-width ratio are utilized, and the addition of body length is shown to further increase accuracy. It is also shown that improving the precision of the measurements leads to increased identification accuracy.

Insect migration provides major ecosystem services, and sometimes, migratory pests cause serious crop damage and yield loss. Species identification is critically important in studies of insect migration, for both entomologists and pest managers. Radar is an effective means of detecting insect migrants. Current entomological radars usually operate at X-band, and signal amplitude information is used to estimate body mass and wing-beat frequency, which can then be used to categorize migratory insects into broad taxon classes. To improve the identification performance, this paper presents a novel radar method of measuring insect mass and body length. The multifrequency radar cross sections (RCS) of insects at X-band and Ku-/K-band are fully investigated, and the comprehensive relationship between RCS and insect morphological parameters provides an improvement in the estimation of insect mass. More importantly, estimations of body length can also be realized with an accuracy of 84% based on experimental data acquired by a vector network analyzer in a microwave anechoic chamber. If multifrequency RCS measurements can be obtained by radar in the future, then highly accurate estimations of insect mass and body length will be possible, although it is currently still a challenge to build a radar capable of making the required measurements over such a wide frequency range.

Migratory insect identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision.