Shinichi Hirako
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Featured researches published by Shinichi Hirako.
IFAC Proceedings Volumes | 2000
Sakae Shibusawa; H. Sato; Akira Sasao; Shinichi Hirako; Atsushi Otomo
Abstract Key points in the revised spectrophotometer design were: use of a RTK-GPS, improved soil penetrator to ensure a uniform soil surface under high speed conditions, and all units are re-arranged for compactness. Test runs proved that the traveling speed could be increased up to about at least 1 m/s, and that the reflectance can also be used for generating soil maps. Derivative operation could eliminate biases on the measured reflectance due to surface disturbance, which enabled to evaluate at least soil moisture and EC.
Engineering in agriculture, environment and food | 2013
Eiji Morimoto; Shinichi Hirako; Hitoshi Yamasaki; Mitsutaka Izumi
Abstract The objective of this paper was to provide the development of on-the-go soil sensor for rice transplanter, particularly from a perspective of precision agriculture applications. Ultrasonic sensor, electrodes and platinum resistance thermometer were employed for topsoil depth (TD) and apparent electrical conductivity (ECa) measurement. Soil fertility value (SFV) defined as new soil parameter, which consisted of EC a / TD. The results of field test revealed that the developed equipments could measure the TD (R 2 = 0.999), and the SFV had a strong relationship with measured EC (R 2 = 0.937).
Automation Technology for Off-Road Equipment Proceedings of the 2004 Conference | 2004
Eiji Morimoto; Sakae Shibusawa; Toshikazu Kaho; Shinichi Hirako
Real-time soil sensor (RTSS) was built and tested, making use of a near-infrared spectrophotometer, which offered a convenient and quick method for in-situ soil organic matter (SOM), total nitrogen (TN), pH and moisture content (MC) measurement. The sensor could collect a spectrum absorbance of soil (i.e. 500-1650 nm with 7 nm interval). Neural network was used for making prediction model for each soil component. The training and testing of neural network was based on 1300 dataset was taken by the RTSS from 7 location of Japan where included paddy and upland crop field. Input variables represent spectrum absorbance of the points of interest, while the output variables represent SOM, TN, pH and MC data of the points of interest, which was analyzed in the laboratory. After the neural network has been successfully trained, its performance was tested on a separate testing set. The result of MC, pH, SOM and TN prediction indicated that the NN model validated coefficient of determination of R2=0.91, 0.75, 0.95 and 0.96, respectively.
Archive | 2008
Tomoki Iitawaki; Yusaku Sakoda; Muneo Tokita; Shinichi Hirako
Archive | 1989
Shinichi Hirako; Yoshihiro Nakatsuji
Archive | 1992
Shinichi Hirako
Archive | 1999
Sakae Shibusawa; Atushi Ohtomo; Shinichi Hirako
Archive | 1998
Shinichi Hirako; Tomoki Kitawaki; Yusaku Sakota; Muneo Tokita; 知己 北脇; 進一 平子; 宗雄 時田; 勇策 迫田
Journal of the Japanese Society of Agricultural Machinery | 2001
Imade Anom Sutrisna Wijaya; Sakae Shibusawa; Akira Sasao; Shinichi Hirako
Archive | 1990
Shinichi Hirako