| Project/Area Number |
09556054
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 展開研究 |
|---|
| Research Field |
農業機械学
|
|---|
| Research Institution | Tokyo University of Agriculture & Technology |
|---|
Principal Investigator |
SASAO Akira Tokyo University of Agriculture & Technology, Agriculture, Professor, 農学部, 教授 (70032993)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
ATODA Oichi Tokyo University of Agriculture & Technology, BASE, Professor, 大学院・生物システム応用科学研究科, 教授 (00107533)
SAKAI Kenshi Tokyo University of Agriculture & Technology, Agriculture, Associate Professor, 農学部, 助教授 (40192083)
SHIBUSAWA Sakae Tokyo University of Agriculture & Technology, Bio-Appli. & Systems Engi. (BASE), Associate Professor, 大学院・生物システム応用科学研究科, 助教授 (50149465)
|
|---|
| Project Period (FY) |
1997 – 1999
|
| Project Status |
Completed (Fiscal Year 1999)
|
| Budget Amount *help |
¥4,200,000 (Direct Cost: ¥4,200,000)
Fiscal Year 1999: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1998: ¥2,700,000 (Direct Cost: ¥2,700,000)
|
|---|
| Keywords | Precision Farming / Soil parameter / Real-time-soil sensor / NIR spectral / Stock competition model / Farm management system / Distinguish stocks / Auto-mapping / テクスチャ画像 / サンプリングサイズ / 時空間変動 / 雑草-作物競合モデル / ロトカ-ボルテラ方程式 / 圃場マッピング |
|---|
| Research Abstract |
1) A tractor-mounted spectroscopic soil sensor developed has enabled to real-time detection of the sub-soil reflectance continuously at depths of 15 to 40 cm as well as to recording the location in the field. It provides spectral reflectance of soil over 400 to 1700 nm wavelengths, a visible to NIR range, which are available for predicting soil moisture, soil organic matter content, nitrate nitrogen content, electric conductivity and pH. The proposed soil sensor will make it possible to increase the accuracy in soil parameters mapping, in addition to time and labor saving for the works.
2) An algorithm for farm work scheduling was developed based on managing the risk of daily weather variation. The risk was evaluated by costs which were then minimized in the optimization. Genetic algorithms were utilized for optimization in order to attain flexibility.
3) Density dependence and symmetrical competition were dealt with by the modified Lotka-Volterra model. Distance function was incorporated into the model and parameter studies based on the model were conducted. Feasibility of the logistic model was discussed. Experimental data for clover-weed competition process were obtained and nonlinear regression was conducted based on the non-symmetrical model. The symmetrical model showed good agreement with experimental data.
4) We treat a picture of micro-selected to constitute a random texture and adopted as a feature extractor. 20 feature elements among 45 are classified are selected to constitute a linear discriminative function. 98% of training sections are classified correctly and 88% of test sections from photographs of mixed vegetation of both stocks agreed with human judgment.
|
