Noble, Scott D.Bui, Francis M.2017-09-262020-09-262017-092017-09-26Septemberhttp://hdl.handle.net/10388/8148The trend towards precision agriculture has led to the advent of a new generation of modern equipment for agriculture. Pneumatic conveying systems are widely used for seeding operations in modern agriculture. This thesis considers the problem of seed velocity measurement and counting seed number in the pneumatic conveying process. For the owing seed velocity measurement, the experimental object was wheat seed. Tests were performed with air velocities of 12, 15, 20, 25, and 30 m/s in a 57.3 mm acrylic pipe while the seed mass ow rate was increased from 1 kg/min to 6 kg/min in 1 kg/min increments. All measurements were taken 10 meters downstream of the feed point of the rotary feeder into the air steam. The proposed method of velocity measurement is based on the cross-correlation algorithm. Two di erent active start points of the cross-correlation have been developed, one is the xed time window, and the other is the threshold detection. The horizontal velocity of the seeds and the slip ratio were calculated from the results. Beside some clumping-seed testing groups, the slip ratio between the total seeds velocity and the air velocity was relatively constant at approximately 0.63. For counting the number of seeds, the experimental objects were wheat and canola, and the tests were taken in the secondary pipeline of the seed drill system. One contribution of this part is signal denoising using compressive sensing. Compressive sensing provides a feasible method based on the sparsity of the seed signal. The other contribution of this part is utilizing pattern recognition technique for counting number. Features were extracted from the seed signal, which are Threshold Detection, Full Width Half Maximum, Cluster Width, Peak and Valley Detection, Number of Turns, and Energy Comparison. Four of them was selected to be applied in the experiments. Multiple classifi er approach was also developed in the classifi cation task of the pattern recognition. The counting accuracy for all of the wheat groups were higher than 92% and for the canola groups were higher than 96%.application/pdfVelocity Measurement, Counting, Electrostatic Sensor, Signal Denoising, Pattern Recognition, Cross-CorrelationThesis2017-09-26