Plant-soil feedback (PSF) describes the reciprocal interactions between plants and soil biota [1
]. Plants and their associated microorganisms influence soil properties, such as mineral nitrogen concentration and organic matter content, as well as the abundance of plant pathogens and mutualists [2
]. The net effect of these changes can enhance or suppress the performance of succeeding plants relative to fallow soil [3
]. Understanding PSF mechanisms is necessary to avoid the risk of negative PSF and generate potential positive PSF by applying well-matching crop rotations in agriculture systems [4
]. Most PSF field studies are dependent on time-consuming and laborious destructive sampling methods which constrain the scale at which these studies can be performed [6
]. Therefore, there is a need to develop new non-destructive methodologies that enable the investigation of PSF mechanisms at a high resolution under actual field conditions and at realistic spatial and temporal scales [4
Remote sensing is used to study ecological phenomena and for applications in precision farming, which involves the use of sensors and information technologies to bring data from multiple sources to support decisions associated with crop productivity and a more efficient use of farm inputs, such as fertilizers and herbicides [9
]. Data can be acquired by unmanned aerial systems (UAS), otherwise known as drones, with a more flexible spatial and temporal resolution compared to other remote sensing platforms [13
]. Remote sensing technologies with high spatial and temporal resolutions allow testing PSF in the field, which minimizes and ultimately discards the need to destructively sample crops and generalize from a limited number of samples [8
]. This is not only relevant for PSF research but also for assessment of crop productivity, which is based on multiple parameters such as dry biomass, height, and volume [11
Crop parameters such as canopy cover, canopy biochemical composition, pigment concentration, and vegetation indices can be derived using high-resolution cameras and hyperspectral sensors [16
]. Recent work showed that UAS carrying a hyperspectral sensor can be used to characterize plant traits and assess PSF effects of different cover crop treatments on a succeeding main grain crop [8
]. Three-dimensional data can be obtained from light detection and ranging (LiDAR) or digital aerial photogrammetry [16
]. Both LiDAR [16
] and digital aerial photogrammetry [19
] enable direct measurement of crop dimensions and indirect measurement of above ground biomass and biophysical parameters. Although unable to return points from below the canopy, multispectral UAS are more affordable and require a smaller payload and battery capacity compared to hyperspectral and LiDAR UAS [22
]. Another advantage is that digital aerial photogrammetry provides high-resolution orthophoto mosaics in addition to accurate digital surface models.
There is potential for integration of crop discrimination methods, which take advantage of the high-resolution orthophoto mosaic, and estimation of crop dimensions. This enables incorporation of meaningful individual crop or plot objects, rather than pre-defined sample plots, in further calculation of crop dimensions. Object detection methods, such as template matching, are used in remote sensing to determine the amount of objects on an image and predict their positions [25
]. Template matching is among the earliest and simplest of such methods but usually results in a high commission error in more complex images [26
]. In recent work, a workflow was developed in which template matching was combined with an object-based image analysis (OBIA) object detection approach to overcome this problem [26
]. A common base of OBIA is image segmentation, but it also incorporates other concepts that have been used for decades in remote sensing such as feature extraction, edge-detection, and classification [28
]. A crucial advantage of an object approach over a pixel approach is the additional spatial dimension for objects such as distance, morphology and topology [29
]. Grouped pixels can characterize fields remarkably better than single pixels using high-resolution imagery [32
], several studies have shown OBIA produces higher accuracies compared to pixel-to-pixel analysis for thematic mapping [33
In this study, the suitability of UAS-based optical remote sensing to measure differences of crop productivity of a leafy vegetable crop between multiple soil treatments (different cover crop treatments preceding the main crop) was assessed using an OBIA approach. Individual main crop detection and crop area segmentation and classification were performed based on an orthophoto mosaic. Mean crop volume was calculated for the experimental plots using the segments, detected crops, and a digital surface model. The developed methodology demonstrated the suitability of UAS and digital aerial photogrammetry for measuring crop productivity during crop growth in a non-destructive way and for application in large-scale PSF research.
Endive crops were well covered by the OBIA-based vector objects, with a detection and accuracy rate of 88.3% and 85.4%, respectively (Table 2
). The commission error was remarkably low, inclusion of bare soil or grass between the crops in the segmentation results was scarce, which is illustrated by the four experimental plots in Figure 5
. Crops surrounded by around 3–6 cm of bare soil or grass were represented by an individual vector object. Objects represented between 1 and 101 individual crops with an average of 48.8 individuals. Before automated removal of omission errors, 201 vector objects were segmented correctly while 138 objects were located outside the experimental plots.
Template matching resulted in 21,500 matches but after integration with the vector objects, a high detection rate and overall accuracy were achieved of 99.9% and 99.8% respectively (Table 2
shows the statistical relation between in situ crop biomass and crop volume is higher than between in situ
crop biomass and crop area. The figure includes the Pearson correlation coefficients for crop area (R = 0.61) and crop volume (R = 0.71) indicating a positive and strong relation with in situ biomass.
In situ crop biomass and estimated crop volume showed a comparable pattern but differences between treatments are clearer for estimated crop volume (Figure 7
). Based on mean crop volume estimates, C. endivia
crops receiving Rs WCC treatment seem to be most productive with a crop volume of 11,281 cm3
, right). Only the Lp and Lp+Tr cover crop treatments resulted in C. endivia
plants that were significantly different (smaller) compared to plants grown after the cover crop Rs (Tukey’s test, p
< 0.05). The Lp treatment resulted in lowest C. endivia
crop volume of 4,369 cm3
, which is significantly different from all other treatments, including its mixture Lp+Tr. Lp is followed by Lp+Tr and Tr with a volume of 8,351 cm3
and 9,242 cm3
, respectively. Although the difference between the monoculture Rs and its mixture Rs+Vs is clearly visible in the graph, it is not significant.