Abstract
Accurate prediction of Arsenic concentration is important for food safety and precision farming. We explore the feasibility of predicting Arsenic concentration in rice plants from hyperspectral data using random forests (RF) in the Arsenic polluted farm lands. Canopy spectral measurements from rice plants were collected using ASD field spectrometer in Suzhou, Jiangsu Province. Rice plants were collected for chemical analysis of Arsenic concentration. Prediction of Arsenic concentration was achieved by a random forests approach. The results show that the random forests approach achieved an R 2 value of 0.84 and an MSE value of 3.97. The results indicate that it is possible to predict concentration of Arsenic in rice plants from hyperspectral data using random forests.
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Lv, J., Liu, X. (2011). Predicting Arsenic Concentration in Rice Plants from Hyperspectral Data Using Random Forests. In: Jin, D., Lin, S. (eds) Advances in Multimedia, Software Engineering and Computing Vol.1. Advances in Intelligent and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25989-0_96
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DOI: https://doi.org/10.1007/978-3-642-25989-0_96
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