Main Article Content
Groundwater is a main source for supplying drinking water. High concentration of fertilizer originated in soils accumulated through irrigation water causes negative impacts on the agricultural environment, soil-grass quality, livestock and fishery production and on the food chain. Fertilizer plays an important role in increasing agricultural production. Over-fertilizing has a negative effect on water quality which in turn negatively affects the health of the people and animals who use it. The purpose of this paper is to develop a methodology that has the potential to reduce the amounts of fertilizer used and thus to have secondary environmental benefits. This is done by using Unmanned Aerial Vehicles (herein after UAVs). The authors conducted experiments in both Hokkaido and Miyakojima, however in this paper, forage crop management in Hokkaido is discussed. UAVs equipped with RGB and near infrared cameras that take Blue Normalized Difference Vegetation Index (BNDVI) images fly over the cropland. Orthorectifying Aerial Photographs are obtained from both of RGB and BNDVI images. Comparing several images, the resulting data can be used, the amounts of fertilizer needed can be optimized by analyzing the spatial growth patterns of the cropland. The authors offer this paper here to stimulate research interest and contacts in the cross disciplinary fields of agriculture management, environmental issues and human health.
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
Novotny, V. (1994). Water quality: prevention, identification and manage-ment of diffuse pollution. Van Nostrand-Reinhold Publishers.
Hilker, T., M.A. Wulder and N.C. Coops. (2008). Update of forest inventory data with LiDAR and high spatial resolution satellite imagery. Can. J. Remote Sens. 34(1): 5–12. https://doi.org/10.5589/ m08-004
Lu, S., Inoue, S., Shibaike, H., Kawashima, S., Yonemura, S., & Du, M. (2015). Detection potential of maize pollen release stage by using vegetation indices and red edge obtained from canopy reflectance in visible and NIR region. Journal of Agricultural Meteorology, 71(2), 153-160.
Matsumura, K. (2017), Preparing an UAV for drift ice observation, Okhotsk Sea and Polar Oceans Research Volume1, 12-15
Matsumura, K., Ito, H., and Midoro, T., (2016), Developing crop index at Okohotsk region based on UAV, TOUFUTSU, Tokyo University of Agriculture, Vol. 19, 41-49. In Japanese
Pajares, G. (2015). Overview and current status of remote sensing applications based on Unmanned Aerial Vehicles (UAVs ). Photogramm. Eng. Rem. S. 81(4): 281–329. https://doi.org/10. 14358/ PERS.81.4.281
Siebert, S. and J. Teizer. (2014). Automation in Construction Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automat. Constr. 41: 1–14. https://doi.org/10.1016/j. autcon.2014.01.004Glowacka K (2011) A review of the genetic study of the energy crop Miscanthus. Biomass and Bioenergy, 35: 2445–2454.