Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

Abstract

Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

Cover crops as a tool to reduce reliance on intensive tillage and nitrogen fertilization in conventional arable cropping systems

“…Finally, all calculated phenological indexes (maxNDVI, Time Integrated NDVI and GrowthRate) significantly correlated with maize biomass and N uptake (Supplementary Figure S4). This also shows the potential of this approach to predict yield and N uptake over larger areas and therefore potentially be used to adapt fertilization depending on crop needs (Maresma et al, 2016;Nuijten et al, 2019).…”

The role of soils in habitat creation, maintenance and restoration

“…Soils are opaque, yet remote and proximal sensing of bare topsoil and of plants responding to soil variation enables the characterization of soil variation for precision agriculture, limiting negative side-effects of fertilizer use and pest control [75]. Moreover, these technologies can help to better understand and quantify plant-soil feedback interactions in the field and to integrate beneficial ecological interactions in land management from local to regional scale [69,76,77]. Also for monitoring biodiversity, sensing technology is an asset especially when used in combination with in situ activity sensors and DNA barcoding [78,79].…”

A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining

“…These approaches have seen relatively little application for detecting ASM, but a successful implementation for general rainforest LULC change analysis (including mining) is found in Souza-Filho et al (2018) with commission/omission errors in the range of 10-30%. However, finding adequate parameters for the initial object segmentation can be labour intensive and relies on good domain knowledge (Nuijten et al, 2019). All methods to date have required a significant level of human input, including feature and segmentation parameter design, manual cluster selection and manual relabelling of incorrect pixels.…”