AUTOMATED DETECTION OF INTERNAL INSECT INFESTATIONS IN WHOLE WHEAT KERNELS USING A PERTEN SKCS 4100

Abstract

The wheat industry is in need of an automated, economical, and rapid means of detecting whole wheat kernelswith internal insect infestation. The feasibility of the Perten Single Kernel Characterization System (SKCS) to detect internalinsect infestations was studied. The SKCS monitors compression force and electrical conductance as individual kernels arecrushed. Samples of hard red winter (HRW) wheat and soft red winter (SRW) wheat infested with rice weevil [Sitophilus oryzae(L.)] and lesser grain borer [Rhyzopertha dominica (F.)] were run through the SKCS and the conductance/force signals savedfor post-run processing. Algorithms were developed to detect kernels with live internal insects, kernels with dead internalinsects, and kernels from which insects have emerged. The conductance signal was used to detect live infestations and theforce signal for dead and emerged infestations. Live insect detection rates were 24.5% for small-sized larvae, 62.2% formedium-sized larvae, 87.5% for large-sized larvae, and 88.4% for pupae. The predicted, and observed, false positive (soundkernels classified as infested) rate was 0.01%. Dead insect detection rates were 60.7% for large-sized larvae, 65.1% forpupae, and 72.6% for kernels where the insect emerged. The false positive rate of the dead insect detection algorithm rangedfrom 0.2% for SRW to 0.5% for HRW. In all cases, insect detection rates were higher for rice weevil than lesser grain borer.The classification algorithms were robust for a wide range of moisture contents.