Automated detection of hidden internal insect infestations in wheat kernels using electrical conductance

Abstract

The wheat industry is in need of an automated, economical, and rapid means to detect whole wheat kernels internally infested with insects. The feasibility of the Perten Single-Kernel Characterization System (SKCS) to detect internal insect infestations was studied. The SKCS monitors compression force and electrical conductance as individual kernels are being crushed. Samples of hard red winter wheat (HRW) and soft red winter wheat (SRW) infested with rice weevil and lesser grain borer were run through the SKCS and the conductance/crush signals saved for post-run processing. It was found that a discontinuity is often present in the conductance signal of an insect-infested kernel. An algorithm was developed to classify kernels as infested, based on features of the conductance signal. Average classification accuracies for all wheat samples were 24.5% for small-sized larvae, 62.2% for medium-sized larvae, 87.5% for large-sized larvae, and 88.6% for pupae. There were no false positives (sound kernels classified as infested). The classification algorithm is robust for a wide range of moisture contents. Classification accuracy was somewhat better for kernels infested with rice weevils than for lesser grain borer, and classification accuracy was better for HRW than for SRW.