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Lentil (Lens culinaris L. subsp. culinaris) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 106 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot (r = 0.92 and β1 = 0.96) than root rot (r = 0.62 and β1 = 0.67).

The advancement of vital sign radar technology has proven to be a useful tool in assessing various physiological dynamics, including heartbeat and respiration. There remains several signal processing challenges in this field, which include overcoming the nonlinearities and harmonics that populate the power spectrum. Respiration harmonics distort and overwhelm the measurement of heartbeat due to the large signal amplitude. A supervised machine learning algorithm, the gamma filter, offers an efficient, calibration-free solution to model the time series heartbeat signal given respiration and respiration artifacts. The measured signal is provided by a 5.8-GHz quadrature Doppler radar and a modified electrocardiogram signal is used as the ground truth for training the filter. Experimental results show that the heartbeat is independent and separable from respiration and the algorithm can be implemented in real time.

Vital signs radar has proven to be an interesting and useful tool; however it is still limited by a few key problems. One of these is the generation of harmonics due to nonlinearities arising from the large signal amplitude of respiration when compared to that of heartbeat. As a result, harmonics arise in the spectrum which confound accurate measurement of either. The gamma filter is a supervised machine learning based approach that offers a calibration-free and computationally efficient solution for many nonlinear filtering applications. Here, it is demonstrated for the first time as a tool for real-time heart rate estimation using the baseband signal from a non-contact vital sign signal measured from a 5.8-GHz quadrature Doppler radar. Experimental results show that the proposed filter for removing respiration harmonics can accurately measure heart rate even if it is weak or overwhelmed by the respiratory movement.