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The Potential Application of Sensor Fusion and Wavelength Selection on Prediction of Leaf Primordia of Potato Tubers

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800091.(doi:10.13031/aim.201800091)
Authors:   Ahmed M Rady, Daniel E Guyer, William Kirk, Irwin R Donis-González
Keywords:   hyperspectral imaging, interval partial least squares, leaf primordia, potatoes, near-infrared spectroscopy, partial least squares regression, spectroscopic systems, wavelength selection


The possibility of fusing data from visible(VIS)/near-infrared(NIR) spectroscopic and hyperspectral imaging systems was introduced in this study to improve the prediction of primordial leaf count which is a significant sign for sprouting capacity of potatoes. Electronic and lab measurements were conducted on whole tubers of Frito lay 1879 (FL1879) and Russet Norkotah (R. Norkotah) cultivars. Interval partial least square (IPLS) technique was applied to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR) and the best calibration model was chosen using 4-fold cross-validation. Then the prediction models were obtained using separate sets of data. A better prediction was achieved, compared to individual systems models, and the values of correlation coefficient(ratio between performance to deviation or r(RPD) were 0.95(3.01) and 0.96(3.55) for FL1879 and R. Norkotah respectively which represented feasible enhancement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R. Norkotah. Consequently, the proposed study shows a potential to build a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers.

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