JOURNAL of the JAPANESE SOCIETY of AGRICULTURAL MACHINERY
Online ISSN : 1884-6025
Print ISSN : 0285-2543
ISSN-L : 0285-2543
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Volume 72, Issue 6
Displaying 1-18 of 18 articles from this issue
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ESSAY
SUMMARY OF AWARD PAPERS
SPECIAL EDITION
TECHNO-TOPICS
RESEARCH ACTIVITIES
PAPERS (Articles)
TECHNICAL PAPERS
  • Measurenent of Working Resistance using Real-time Soil Sensor
    Toshikazu KAHO, Sakae SHIBUSAWA, Hiroki UMEDA, Shin-ichi HIRAKO
    2010 Volume 72 Issue 6 Pages 563-569
    Published: November 01, 2010
    Released on J-STAGE: March 14, 2013
    DOIhttps://doi.org/10.11357/jsam.72.563
    JOURNAL FREE ACCESS
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    We investigated the performance of instruments to determine the working resistance of the real-time soil sensor. The working resistance was estimated from the moment acting on the shank with a chisel implement, and the vertical and horizontal components of force acting on the chisel tip to cut the soil. Two measuring devices with a one-way-detecting compression transducer and a cantilever-shaped round-detecting load transducer were developed to monitor the forces on the chisel tip. The one-way-detecting compression transducer detected hysteresis in the loading and unloading due to friction effects induced by vertical force. The cantilever type showed a correlation coefficient of 0.997 with the horizontal force and 0.994 with the vertical force in situ. A field experiment yielded a correlation coefficient of 0.661 between the horizontal load measured by the cantilever type and the resistance of the cone penetrometer.
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  • Development of a Reflecting Near-Infrared Spectroscopy Sensor Mounted on a Head-Feeding Combine for Measuring Rough Rice Protein Content
    Yasuyuki HIDAKA, Eiji KURIHARA, Kazunobu HAYASHI, Takahiro NODA, Yoh N ...
    2010 Volume 72 Issue 6 Pages 570-577
    Published: November 01, 2010
    Released on J-STAGE: March 14, 2013
    DOIhttps://doi.org/10.11357/jsam.72.570
    JOURNAL FREE ACCESS
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    We developed a near-infrared (NIR) sensor mountable on head-feeding combines for measuring rice protein in real time while harvesting. The developed sensor employed reflectance optic instead of transmission one which usually is used since : 1) it operates under heavy duty condition such as vibration and dust, 2) it measures high-moisture, low-fluidity paddy, and 3) low light transmission absorbed by husk was anticipated. The light source was a tungsten halogen lamp, and a diffusion cylinder was installed so that uniform light would illuminate the sample. The detector could measure the spectrum from 740nm to 1140nm with a post-dispersive grating spectrophotometer using a diffraction grating. We made a calibration curve based on brown rice protein from the spectrum of rough rice examined in a laboratory. The calibration curve accuracy was r=0.87 and SECV (Standard Error of Cross-Validation)=0.47%. In the measurement method adopted, the sensor loaded the rough rice into a wide sample chamber by natural gravity and analyzed the loaded grain in the bottom using a reflected near-infrared signal. The developed sensor was able to measure the protein content of brown rice from the spectra of rough rice under severe conditions, such as the high-vibration, high-dust environment of harvesting. In addition to the protein content, the rice weight and moisture content could be displayed on the monitoring terminal in real time. The accuracy of measuring protein content in the field examination was r=0.65 and SEP (Standard Error of Prediction)=0.22%. The SEP was far better than SECV of the calibration ; protein content fell in a narrow range in this field test. So we concluded that the actual accuracy would be the same as the calibration.
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  • Study on an Electrostatic Pesticide Spraying System for Low-Concentration, High-Volume Applications
    ——Development of an Electrostatic Spraying Robot for Melon Greenhouses——
    Suguru YAMANE, Masahiro MIYAZAKI, Hidefumi SAITO, Kazuhiro OHMURA, Mam ...
    2010 Volume 72 Issue 6 Pages 578-586
    Published: November 01, 2010
    Released on J-STAGE: March 14, 2013
    DOIhttps://doi.org/10.11357/jsam.72.578
    JOURNAL FREE ACCESS
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    The objective of this study was to develop an autonomous spraying system with high deposition performance for large-scale melon greenhouses. A spraying robot with low-concentration, high-volume electrostatic spraying nozzle was tested. The robot was equipped with a total of eight electrostatic spraying nozzles with a per-nozzle discharge rate of 0.56L/min and charge to mass ratio of −0.45mC/kg. The robot could apply pesticide stably without operators by being guided by magnetic tape on the greenhouse floor. The deposition indexes of electrostatic spraying on the backside and underside of water-sensitive paper placed in front of the melon community were 8.0-10, and were two-fold higher than those of non-electrostatic spraying. At the back of the melon community, deposition indexes for the electrostatic method were significantly higher than those for the non-electrostatic method. When the application rate was increased from 100 to 300L/10a, the deposition index of electrostatic method became higher. There was no significant difference in the insect pests control effect between the robot and conventional manually spraying method. In the field test, the effective field capacity of the robot was 3.8a/h at the application rate of 450L/10a.
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  • Development of a Tomato Harvesting Robot with a Vision System Using Specular Reflection (Part 1)
    ——Selection of Hand-Approach Direction by Image Processing——
    Tomohiko OTA, Takashi YAMASHITA, Shigehiko HAYASHI, Takashi KOMEDA
    2010 Volume 72 Issue 6 Pages 587-594
    Published: November 01, 2010
    Released on J-STAGE: March 14, 2013
    DOIhttps://doi.org/10.11357/jsam.72.587
    JOURNAL FREE ACCESS
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    With the aim of achieving automation of tomato harvesting, the authors prototyped a detecting system that selects the approach direction for the harvesting hand and clarify the accuracy. In operation, the processing program first extracts an entire tomato cluster, which consists of red colored fruit and green colored fruit. Next, the pixels of 9 meshes—around and to the forward direction—of the nearest red colored tomato to the hand are calculated. The direction from which the hand can easily grasp the tomato is selected automatically based on the pixel information. Image-processing experiments have shown that in 89-96% of cases, the system succeeded in determining an approach direction for clusters bearing 2-4 tomatoes.
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  • Development of a Tomato Harvesting Robot with a Vision System Using Specular Reflection (Part 2)
    ——Vehicle-Motion Control by Motion-Image Processing and Harvesting Experiment——
    Tomohiko OTA, Takashi YAMASHITA, Shigehiko HAYASHI, Takashi KOMEDA
    2010 Volume 72 Issue 6 Pages 595-603
    Published: November 01, 2010
    Released on J-STAGE: March 14, 2013
    DOIhttps://doi.org/10.11357/jsam.72.595
    JOURNAL FREE ACCESS
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    With the aim of achieving automation of tomato harvesting, the authors prototyped a tomato-harvesting robot with a vehicle-motion control system that halts the robot in front of red colored tomatoes, and a manipulator-control system that determines a suitable approach route for the robot harvesting hand by analyzing the cluster, and clarify the performance. The results of the experiments indicated the position to the camera centerline was -51-94mm. The results of the harvesting experiments showed the grasping success rate was 81-95%,when at least half of the objective tomato was visible to the camera with 1-4 tomatoes in the cluster. The harvesting success rate was 44-90%. The average execution time for harvesting was 27.6s/fruit.
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