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Development of Spatial-Temporal Model of Soil-Plant System and Farm Management System

土壤-植物系统时空模型及农场管理系统的开发

基本信息

批准号：
09556054
负责人：
SASAO Akira
金额：
$ 2.69万
依托单位：
Tokyo University of Agriculture & Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1999
项目状态：
已结题

项目摘要

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.

1)研制的拖拉机安装的光谱土壤传感器能够实时检测15至40厘米深度的地下土壤反射率，并记录田间位置。它提供了土壤在400至1700 nm波长范围内的光谱反射率，这是一个可见的近红外范围，可用于预测土壤水分，土壤有机质含量，硝态氮含量，电导率和pH值。拟议的土壤传感器将有可能提高土壤参数制图的准确性，2）提出了一种基于天气变化风险管理的农田作业调度算法。通过成本来评估风险，然后在优化中将成本最小化。利用遗传算法进行优化，以获得柔性。3）采用改进的Lotka-Volterra模型处理密度依赖和对称竞争问题。在模型中引入距离函数，并对模型进行了参数研究。探讨了Logistic模型的可行性。通过对苜蓿-杂草竞争过程的实验数据进行分析，并根据非对称模型进行非线性回归。对称模型与实验数据吻合较好。4）对一幅微选图像进行随机纹理处理，作为特征提取器。在45个被分类的特征元素中选择20个来构成线性判别函数。98%的训练部分被正确分类，88%的测试部分从两个股票的混合植被的照片同意与人类的判断。