Using Internet of Things (IoT), Near-Infrared Spectroscopy (NIRS), and Hyperspectral Imaging (HSI) to Enhance Monitoring and Detection of Grain Pests in Storage and Handling Operators

Abstract

To reduce the use of insecticides, silo operators are reconsidering their practices and implementing integrated pest management (IPM) to manage insect infestations. IPM requires the early detection of insects to react before infestation spread or to isolate infested lots. Depending on their position in the storage and handling chain, operators will favor monitoring or rapid detection tools. To simplify monitoring in storage, an internet-connected trap has been designed. It includes a camera located above a tank that allows for the captured insects to be counted. A total of 89 traps were installed in elevators for a proof-of-concept phase. Compared to sample monitoring, the traps detected an average of three additional insect species in an infested batch. To improve the detection of insects in wheat, methods for detecting and quantifying live adult insects (Sitophilus oryzae, Rhyzoperta dominica, and Tribolium confusum) using NIRS and HSI have been developed. The used instruments, a near-infrared spectrometer and a hyperspectral camera, allow for an in-flow analysis, which reduces sampling errors. The cross-validation errors of the NIRS models ranged from 2.44 insects/kg to 2.56 insects/kg, and the prediction error of the HSI ones ranged from 0.70 insect/kg to 2.07 insect/kg, depending on the insect species.