Assessing Soil and Crop Characteristics at Sub-Field Level Using Unmanned Aerial System and Geospatial Analysis

Abstract

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.