Terahertz technology has been rapidly expanding both in its use and in attention given to it. A possible application is in corn breeding, specifically when the doubled haploid method is used. Haploid kernels are induced in corn plants in order to decrease the time to reach homozygous genetic corn lines. These haploid kernels must be separated from the surrounding diploid kernels; presently this is done by extensive manual labor using visual markers. This work represents a proof of concept that haploid classification can be automated using terahertz time domain spectroscopy (THz-TDS) paired with a machine learning algorithm, like a probabilistic neural network (PNN).

In this work, a THz-TDS system was used to collect time domain waveforms from a sample of mixed haploid and diploid corn kernels. Variabilities in beam focus and kernel geometry were reduced by taking multiple scans at different heights and at many scan positions. A watershed image segmentation technique was used to reduce the data quantity and organize them by kernel. The waveform data were then transformed to the frequency domain and further classified by PNN with a training set random subsampling technique. Leave-one-out and K-folds cross-validation procedures were used to train the model. The preliminary results show promise yielding an average classification rate of 75 percent correct by 5-fold cross-validation. THz ability to penetrate material leads to immense potential for similar applications in nondestructive evaluation, biomed, and agriculture.