Practicing agriculture is a multiparametric and for this reason demanding task. It involves the management of many factors and thorough strategic planning in a highly variable and uncertain environment. Crop production is a function of agricultural practices as applied in natural resources, such as soil and plants. When referring to conventional agriculture, variability in these resources is neglected, as any field is treated homogenously. On the other hand, site-specific crop management, which was promoted through the advance of technologies, regarding collecting and analyzing data and applying agricultural decisions at a sub-field level, considers field spatial and temporal variations. Localizing inputs in a field rationalizes agricultural waste management and offers promising perspectives towards a circular economy. In this context, two cotton fields in central Greece were selected for this study. During the growing period, reflectance data were acquired, before planting at the end of April, and 100 days after planting at the end of July, with a commercial unmanned aerial system (UAS). The fields were grid sampled for soil (clay content, pH, calcium carbonate percentage, organic matter, total nitrogen, and electrical conductivity) and plant properties (total nitrogen, potassium, iron, copper, and zinc) determination. All data were manipulated through geographical information systems (GIS) and further participated in principal component analysis (PCA) application. PCA revealed important relations and groupings between soil reflectance and organic matter, carbonates, and clay content in both fields (72 to 87% of the total variance in the initial parameters was explained by the extracted components). However, in plant data, the resulting components accounted for less variability in initial data (62 to 72%). PCA resulting scores were introduced in the Fuzzy c-means clustering algorithm, which categorized sub-areas of the fields into two discrete zones per field. Zoning, in the case of soil properties, was accompanied with the statistically important (p < 0.01) discrimination of the mean values (except for total nitrogen and pH), implicating a promising zonal management scheme. The zone delineation process regarding plant properties yielded areas that did not share statistically significant variations, except for the mean values of iron concentration (p < 0.01). According to the results, spatial variations were revealed across the fields, mostly in soil properties, which can be directly monitored through aerial reflectance data. The applied methodology can be used in extension services or by agronomists for producing fertilizer application maps. Further, when integrated with a broader spatial decision support system, it can be used by policy makers for adapting circular economy strategies in crop production.

中文翻译：

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利用无人机和地理空间分析法评估亚田间土壤和作物的特征

实行农业是一项多参数的工作，因此是一项艰巨的任务。它涉及到在高度变化和不确定的环境中管理许多因素并进行全面的战略计划。作物生产是应用于土壤和植物等自然资源的农业实践的功能。当提到常规农业时，这些资源的可变性被忽略了，因为任何领域都被同质地对待。另一方面，由于技术的进步而促进了针对特定地点的作物管理，该管理涉及在亚田地一级收集和分析数据以及应用农业决策，它考虑了田地的时空变化。在一个领域中对投入物进行本地化可以合理化农业废物管理，并为循环经济提供有希望的观点。在这种情况下，这项研究选择了希腊中部的两个棉田。在生长期间，使用商业无人航空系统（UAS）获取反射数据，该数据在4月底种植之前和7月底种植之后100天。对这些田地进行网格采样，以确定土壤（粘土含量，pH，碳酸钙百分比，有机质，总氮和电导率）和植物特性（总氮，钾，铁，铜和锌）的含量。所有数据都通过地理信息系统（GIS）进行了处理，并进一步参与了主成分分析（PCA）的应用。PCA揭示了土壤反射率与有机质，碳酸盐，两个字段中的粘土含量（初始参数总方差的72％至87％由提取的成分解释）。但是，在工厂数据中，所得成分在初始数据中的变异性较小（62％至72％）。在模糊c均值聚类算法中引入了PCA结果分数，该算法将字段的子区域分为每个字段两个离散区域。就土壤性质而言，区划伴随着统计学上的重要意义（p <0.01）区分平均值（总氮和pH值除外），这意味着有希望的分区管理方案。关于植物特性的区域划分过程产生的区域除铁浓度的平均值外（p <0.01）没有统计学上的显着差异。根据结果​​，整个田地都显示出空间变化，主要是土壤性质，可以通过空中反射率数据直接进行监测。可以在推广服务中使用该应用的方法，也可以由农艺师使用该方法来制作肥料应用图。此外，当与更广泛的空间决策支持系统集成时，决策者可以使用它来调整作物生产中的循环经济策略。