Abstract
This paper presents a method to assess nitrogen levels, a nitrogen nutrition index (NNI), in corn crops (Zea mays) using multispectral remote sensing imagery. The multispectral sensors used were four spectral bands only. The experiments were compared with nitrogen levels sensed in the field. The corn crops were divided into three nitrogen fertilization levels (70, 140 and 210 \(\mathrm{kg N}\cdot \mathrm{ha}^{-1}\)) into three replicates. In this sense, we propose a method to infer nitrogen levels in corn crops by using airborne multispectral sensors and machine learning techniques. The presented results offered a simple model to estimate nitrogen with low-cost technologies (UAVs and multispectral cameras only) in small to medium size areas of corn crops.
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Arroyo, J.A., Gomez-Castaneda, C., Ruiz, E., de Cote, E.M., Gavi, F., Sucar, L.E. (2017). Assessing Nitrogen Nutrition in Corn Crops with Airborne Multispectral Sensors. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_28
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DOI: https://doi.org/10.1007/978-3-319-60045-1_28
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