Grant-in-Aid for Scientific Research (B).
|Allocation Type||Single-year Grants|
|Research Institution||Osaka Prefecture University(2000)|
FUJIURA Tateshi Osaka Prefecture University, Graduate School of Agriculture and Biological Sciences, 農学生命科学研究科, 教授 (00026585)
北村 豊 島根大学, 生物資源科学部, 助教授 (20246672)
土肥 誠 島根大学, 生物資源科学部, 助教授 (60284022)
石束 宣明 島根大学, 生物資源科学部, 教授 (20314619)
中尾 清治 島根大学, 生物資源科学部, 教授 (40032560)
|Project Period (FY)
1997 – 2000
Completed(Fiscal Year 2000)
|Budget Amount *help
¥6,100,000 (Direct Cost : ¥6,100,000)
Fiscal Year 2000 : ¥1,000,000 (Direct Cost : ¥1,000,000)
Fiscal Year 1999 : ¥900,000 (Direct Cost : ¥900,000)
Fiscal Year 1998 : ¥1,200,000 (Direct Cost : ¥1,200,000)
Fiscal Year 1997 : ¥3,000,000 (Direct Cost : ¥3,000,000)
|Keywords||3-D image / Recognition / Robotics / Farm work / Laser / Crop / Photo Spectral Characteristics / Scanning / 省力化|
The objective of this study is to develop a 3-D vision system to recognize the crop for the automation of the farm work.
Cucumber for the fresh market is harvested selectively by the human labor when the size is suitable for the market. As the fruits grow rapidly, the harvesting job must be done frequently. To automate the cucumber harvesting job, a 3-D vision system was manufactured for the trial purpose and was mounted on a cucumber-harvesting robot. The 3-D vision sensor emitted an infrared laser beam. The beam was reflected by a scanning mirror toward the crop. The reflected light from the crop surface returned to the 3-D vision sensor. Further, it was reflected by another scanning mirror and was focused on a PSD (Position Sensitive Device) by a lens. The distance to each scanning point was calculated by the signals from two anodes of the PSD.The number of pixels was 7500, that is, 60 in horizontal and 125 in vertical. Experiment wis carried out using cucumber models that were made
based on a real cucumber. The location and the size of the fruit could be measured with fairly good accuracy. The location of the peduncle could also be recognized. The 3-D vision sensor was considered to be effective for the cucumber-harvesting robot.
Cucumber was cultivated by trial in following training method.
(1) Inclined trellis training
(2) Training along the inclined pole
(3) Training by hanging the cucumber stalk keeping the stalk inclined but parallel to the crop row
Cucumber fruits on the stalk that was cultivated in the style of No.3 was most visible because the fruit hanged from the stalk and the hiding probability by leaves was small. The training method was considered suitable for the cucumber-harvesting robot.
Recognition experiment of the crisp head lettuce was carried out. The size of the crisp head could be recognized by processing the 3-D image.
Recognition of the cherry tomato was tried by a 3-D vision sensor mounted at the end of end effector. Many of the fruits (including the fruits hidden by the leaf) could be recognized by scanning from various view points with the motion of the end effector. Less