Spatial Analysis of Digital Imagery of Weeds in a Maize Crop

Abstract

Abstract:Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m 2 ) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm-18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches.

Spatial Characteristics of the Fungus Powdery Mildew (Erysiphe neolycopersici) on Tomatoes and its Spread in Industrial Greenhouses

“…Unfortunately, REML can take into account only fixed effects that are linear combinations of the spatial coordinates; it cannot cope with non-linear ones such as the bell-shaped surface of Equation (7). We have therefore had to fall back on the earlier technique of separating the trend from the residuals and estimating their coefficients and parameters independently thereafter.…”

“…There have been many investigations of the distributions of weeds, plant parasites and crop diseases in the field with attempts to model them statistically and map them with a view to identifying the processes that have brought them about. Recent examples in which the most up-to-date methods of spatial analysis have been applied include bacterial blight in rice [2], virus disease in tomatoes [3], rust in coffee [4], crown atrophy in coconut [5] and weed infestation in cereal crops [6,7]. The most relevant recent example in the context of our investigation is that by Liu et al [8] on microclimatic conditions combined with theoretical disease spread in greenhouses.…”

Dynamics of bacterial blight disease in resistant and susceptible rice varieties

“…The variogram is a tool in spatial statistics that quantifies the spatial dependence in a variable of interest. While it has been widely used in soil science and ecology (Webster and Oliver, 2007;San Martin et al, 2018), it has also been used in plant pathology (Van de Lande and Zadoks, 1999; Bedimo et al, 2007;Nkeng et al, 2017). The variogram is defined as the function that links the expected squared difference of a variable between any two places in a field where Z(x) and Z(x + h) are random variables at positions x and x + h separated by the vector h for all h. It characterizes quantitatively the spatial dependence in the variable (Webster and Oliver, 2007).…”

Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh

“…Quadrat analysis is a relatively straightforward method for studying the spatial arrangement of point locations by counting the frequencies of points occurring in an area [28,29]. Quadrat-based aggregation of data points is a widely used method of spatial statistics utilized across many disciplines related to the spatial sciences, such as ecology [30], crop science [31], information science [32] or GIScience [33]. The technique has also been used to study health outbreaks [34], crime occurrence [35,36], and the spatial patterns of fire events [37].…”