Detection of Corn Kernels Infected by Fungi

Single-kernel reflectance spectra (550 to 1700 nm), visible color reflectance images, x-ray images, multi-spectraltransmittance images (visible and NIR), and physical properties (mass, length, width, thickness, and cross-sectional area)were analyzed to determine if they could be used to detect fungal-infected corn kernels. Kernels were collected from corn earsinoculated with one of several different common fungi several weeks before harvest, and then collected at harvest time. It wasfound that two NIR reflectance spectral bands centered at 715 nm and 965 nm could correctly identify 98.1% of asymptomatickernels and 96.6% of kernels showing extensive discoloration and infected with Aspergillus flavus, Aspergillus niger,Diplodia maydis, Fusarium graminearum, Fusarium verticillioides, or Trichoderma viride. These two spectral bands caneasily be implemented on high-speed sorting machines for removal of fungal-damaged grain. Histogram features from threetransmittance images (blue and red components of color images and another at 960 nm) can distinguish 91.9% of infectedkernels with extensive discoloration from 96.2% of asymptomatic kernels. Similar classification accuracies were achievedusing x-ray images and physical properties (kernel thickness, weight, length). A neural network was trained to identifyinfecting fungal species on single kernels using principle components of the reflectance spectra as input features.