A method for recognition of locust age with few observations based on spectral analysis combined with chemical analysis

Locusts are one of the major pests in the world, causing huge losses to the economy, so it is very important to monitor them effectively. The age recognition of locusts is an important part of public opinion monitoring. At present, manual investigation, the visible-light image processing, and trapping methods are mainly used for monitoring. These methods are highly influenced by the environment and cost and have some limitations. In this paper, a method of recognizing the age of locusts in small samples based on spectral analysis and chemical analysis was proposed. Firstly, the hyperspectral scanning of 180 group locusts (including the 3rd age larva of locust, the 5th age larva of locust and the adult of locust) was carried out during the experiment. The data processing was performed by The Environment for Visualizing Images (ENVI) soft to extract the reflectivity data of the region of interest (ROI) in 400–1000 nm band. Secondly, we extracted and analyzed the chemical composition of the surface material of locusts, and finally found 2,4-ditert-butylphenol which can be used to distinguish different age locusts. We also obtained the content range of 2,4-ditert-butylphenol in the surface substances of three different age locusts through experiments. Finally, 180 sets of spectral data and material composition data were analyzed and processed to build a regression prediction model named MLRSVR between them. The model only needs to input the corresponding spectral data of characteristic bands to predict the content of 2,4-ditert-butylphenol on the locust body surface, so as to recognize the age of locust according to the age range of the composition content. The experiment in this paper shows that the regression prediction model MLRSVR constructed under the spectral data of the characteristic bands of 744 nm, 820 nm, 830 nm, and 885 nm has a good effect, in which R2 reaches 0.97. Compared with the spectral data processed in 400–1000 nm, the data amount is greatly reduced and the computing speed of the model is accelerated. In addition, the range of 2,4-ditert-butylphenol content on the body surface of three different age locusts was (0.16, 0.27), (0.34, 0.91), (1.1, 2.1), respectively. Therefore, the model MLRSVR has excellent learning ability and prediction ability of small sample data. This study provides some help for the monitoring of locusts in the field and some reference for the monitoring of other insects.