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On-the-go Sensing of Chlorophyll Status in Corn

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

Citation:  Paper number  011175,  2001 ASAE Annual Meeting. (doi: 10.13031/2013.3815) @2001
Authors:   S. D. Tumbo, D. G. Wagner, P. H. Heinemann
Keywords:   Precision agriculture, Spectrometer, Chlorophyll, Corn, Neural networks

An on-the-go system for sensing chlorophyll status in corn using neural networks and fiber-optic spectrometry was developed and tested at speed of 0.6 km/h to acquire data on five plots of corn. A neural network model incorporated into the mobile system was trained using statically collected plant-center spectral data and chlorophyll readings acquired by SPAD 502 chlorophyll meter on the some day and in the same field plots. The neural network model showed good correlation between predicted and actual chlorophyll readings of the calibration data set (r 2 =0.85, root mean square error (RMSE) =1.82 SPAD units). The correlation between actual chlorophyll readings and predicted chlorophyll readings of mobile (0.6 km/hr) sensed spectral data was RMSE = 0.76 SPAD units at the tractor speed of 0.6 km/h. The speed of 0.6 km/h was used because of the low download speed of spectral data from the spectrometer to the computer and the need to obtain a large number of spectral data per unit distance.

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