11 December 1998 Precision agriculture with hyperspectral remotely sensed data, GIS, and GPS technology: a step toward environmentally responsible farming
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Abstract
Traditional farming usually resorts to the use of large amounts of fertilizers and other chemicals to meet the needs for food production. These practices ultimately cause dreadful harm to human lives. Precision agriculture has emerged as a valuable and a promising aid to mitigate these adverse effects by helping farmers increase crop yield while sustaining a clean environment. As an outgrowth of the efforts exerted in this field, we conducted a study, using hyperspectral remotely-sensed data, GIS, and GPS to adequately manage the nitrogen (N) fertilizer applications to optimize the crop yield while protecting the environment. Three nitrogen rates (i.e., 40, 80, and 120 kg/ha) from different sources were applied to four replications, with 20 plots (i.e., treatments) in each replication and data were collected at different growth stages of cotton. Statistical analyses indicated significant correlation between the hyperspectral data and the leaf chlorophyll content and crop yield. The results also indicated no significant difference in yield between 80 and 120 kg/ha N application rates, which suggests that high rates of N does not benefit the farmer more than it hurts the environment. GIS analysis visually revealed site specific (using GPS) relationship among these elements. Through this study, our ultimate goal of predicting the N content from non- destructive remotely-sensed observations in order to adequately and rapidly manage the utilization of N fertilizer was achieved, which will benefit farmers and help protect the environment.
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Ahmed Fahsi, Ahmed Fahsi, Teferi D. Tsegaye, Teferi D. Tsegaye, John L. Boggs, John L. Boggs, Wubishet Tadesse, Wubishet Tadesse, Tommy L. Coleman, Tommy L. Coleman, } "Precision agriculture with hyperspectral remotely sensed data, GIS, and GPS technology: a step toward environmentally responsible farming", Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); doi: 10.1117/12.332759; https://doi.org/10.1117/12.332759
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