Fuzzy Classification in the Determination of Input Application Zones

ABSTRACT Correctly interpreting soil fertility and its spatial distribution within an area helps to lessen losses and environmental effects associated with agriculture, to optimize fertilization and liming practices. This study is aimed at using concepts and methods from spatial and temporal analyses to soil fertility and to develop a fuzzy classification methodology in an effort to define input application zones in three conilon coffee harvests. An irregular network with georeferenced points was built in the central region of the farm. Soil samples were collected at 0.00-0.20 m depth within the projections of tree canopies. Geostatistical analysis was used to set up maps in which the variables were shown. In such maps, input and output fuzzy sets were created and applied, as well as rules of inference and determination of to-be-applied logical operators. Fuzzy classification of the area was performed in the three harvests so as to define whether or not inputs were needed. Our main findings show that the N-P-K requirement was spatially dependent in all harvests. By classifying the area using fuzzy logic, it was possible to analyze soil fertility and to indicate the regions having the smallest and greatest needs for N-P-K and liming.摘要 精准解析区域内土壤肥力及其空间分布，可降低农业生产相关的损失与环境影响，同时优化施肥与石灰施用作业。本研究旨在借助时空分析的相关概念与方法开展土壤肥力研究，并开发一套模糊分类方法，以明确三个科尼隆咖啡（conilon coffee）种植季的养分投入施用分区。研究团队在农场中部区域构建了带有地理参考点的不规则采样网络，在树冠投影范围内，于0.00~0.20米深度采集土壤样品。采用地质统计学分析（Geostatistical analysis）方法绘制变量分布图件，在该类图件中构建并应用了输入、输出模糊集，同时确定了推理规则与待使用的逻辑算子。针对三个种植季分别开展区域模糊分类，以判定是否需要施加养分投入。本研究主要结果显示，所有种植季的氮磷钾养分需求均具有空间依赖性；通过模糊逻辑对区域进行分类，可实现土壤肥力分析，并明确氮磷钾与石灰需求量最低和最高的区域。