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

1)研制了一种拖拉机安装的光谱土壤传感器，能够实时连续检测15-40厘米深度的底土反射率，并记录田间位置。它提供了400到1700 nm波长的土壤光谱反射率，这是一个可见到近红外的范围，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。所提出的土壤传感器将有可能提高土壤参数制图的精度，并为工作节省时间和人力。2)提出了一种基于管理每日天气变化风险的农活调度算法。风险通过成本进行评估，然后在优化过程中将成本最小化。3)采用改进的Lotka-Volterra模型处理密度依赖和对称竞争问题。在模型中引入了距离函数，并在此基础上进行了参数研究。对Logistic模型的可行性进行了探讨。获得了三叶草-杂草竞争过程的实验数据，并基于非对称模型进行了非线性回归。该对称模型与实验数据吻合较好。4)对一幅微观选取的图像进行处理，构成随机纹理，作为特征提取。从45个特征元素中选取20个特征元素组成线性判别函数。从两个种群的混合植被照片中，98%的训练段被正确分类，88%的测试段符合人的判断。

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.