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Monitoring Winter Wheat Growth Using Plant Nutrition Active Sensor

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Monitoring Winter Wheat Growth Using Plant Nutrition Active Sensor
Monitoring Winter Wheat Growth Using Plant Nutrition Active Sensor

Abstract: A real-time integrated crop monitoring and application system is needed to be developed for the wheat growth environment. This research developed a ground base-sensing embedded on tractor which monitors the growth of winter wheat by using a couple of plant nutrition active sensors (Crop Spec) and RTK-GPS. In this active sensor, two different infrared wavelengths were used for determination of vegetation index which named S1-Value in this research. In order to consider the reliability of results a test field was cultivated winter wheat in main campus of Hokkaido University and four level of fertilizer were applied to make the difference in crop condition. The 20 points as a ground reference from field were randomly chosen and growth information (grass height, SPAD value, number of stem, nitrogen content and reflectance data) was acquired. By investigating correlation with S1 value and growth information, growth presumption using this plant nutrition sensor and its accuracy were verified. The results showed that there was high relationship between S1 Value and other ground reference data such as grass height, SPAD value, nitrogen Content and reflectance data which acquired by using a Spectroradiometer. So because of easily to use, on-the-go and speed of scanning of field by the Crop Spec, that is recommended for monitoring and estimation of crop growth condition. Keywords: remote sensing, Crop Canopy, Spectroradiometer, SPAD value, winter wheat

1. INTRODUCTION
The global agricultural workforce continues to decrease, with individual workers being responsible for greater area of land. Application of precision farming (PF) is one the solutions to solve this problem. New concepts and new agricultural technology are needed (Noguchi et al., 1999). The need for a crop sensor for monitoring input resource application, especially nitrogen



References: Ahmad, I. S., Reid, J F., Noguchi, N., Hansen, A. C. (1999). Nitrogen sensing for precision agriculture using chlorophyll maps. ASAE paper 993035. B. J. Zebarth and H. Rees. (2003). Mapping Spatial Variation in Potato Nitrogen Status Using the N-Sensor. Acta Hort, ISHS. Dawson C J. (1997). Management for spatial variability. In Precision Agriculture _97, Warwick, 7–10 Sep 1997. Vol. 1. Spatial Variability in Soil and Crop. Ed. J V Stafford. pp. 45–58. BIOS Scientific Publishers Ltd, Oxford, UK. Han-ya. I., Ishii, K., Noguchi, N. (2009). Estimation of moisture content of wheat ear using satellite and helicopter imagery. Pap.Environ.Inf.Sci. Vol.23, 155- 160. Han-ya, I., Ishii, K., Noguchi, N. (2009). Acquisition and analysis of wheat growth information using satellite and aerial vehicle imageries. Proceedings for 3rd Asian Conference on Precision Agriculture, Paper No.11008 (published on CD). Han-ya. I., Ishii, K., Noguchi, N. (2010). Satellite and aerial remote sensing for production estimates and crop assessment. Environment Control in Biology, 48(2), 51- 58. Noboru Noguchi; John G. O`Brien (2003). Remote sensing Technology for Precision Agriculture. Environ. Control in Biol., 41(2), 107-120. Sugiura, R., Noguchi, N., Ishii, K. (2005). Remote sensing technology for vegetation monitoring using an unmanned helicopter. Biosystems Engineering, 90(4), 369-379. Takahashi, T., Yasuoka, Y. (2006). Estimating leaf area index of riverine vegetation using hyper spectral remote sensing and vegetation index. Annual Journal of Hydraulic Engineering, Vol. 50, 1213-1218. V. ALCHANATIS and Z. SCHMILOVITCH. (2005). In- Field Assessment of Single Leaf Nitrogen Status by Spectral Reflectance Measurements. Precision Agriculture, 6, 25-39. Zhang, J. H., K. Wang, R. C. Wang. (2003). Application of chlorophyll meter SPAD in diagnosis nitrogen status and nitrogenous fertilizer in rice. Journal of Northwest A&F University (Natural Science Edition). 31(2): 177-180.

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