G.I. Metternicht, K. Halloran, C. Baldacchino

Department of Spatial Sciences, Curtin University of Technology, Perth, Western Australia, Australia


Precision crop management is a strategy that uses information technologies to integrate data from multiple resources (e.g. GPS, GIS, combine-mounted yield monitors, remote sensing) on decisions associated to crop production. Remotely sensed imagery provides many opportunities in the development of precision crop management techniques. High spatial resolution air- and satellite-borne imagery can aid in the development of information basis to rapidly map spatial variations in crop productivity, assisting managers to find the causes of such variability so that better management strategies can be implemented.


This research deals with two applications of high resolution multispectral remote sensing in precision crop management: crop yield prediction and precision viticulture.


The capability to predict crop yield before harvesting is an important factor, as it enables farm managers to change farming practices throughout the growing season in order to maximise profit and yield, while minimising costs. Remote yield prediction in this research was undertaken using imagery collected by an airborne system (DMSI) developed by SpecTerra Systems, a Perth-based company, able to provide four-bands multispectral imagery in the blue to NIR regions of the spectrum at a spatial resolution varying from 0.25 to 2 meters. Image transformations (e.g NDVIgreen, NDVI, Plant Pigment Index, Photosynthetic Vigour Index) and digital counts from raw bands were statistically regressed against yield data interpolated from yield points collected using a combine harvester and yield monitor, to assess the potential of developing algorithms for yield prediction of wheat, lupins, canola, and oats.


In regards to precision viticulture, the main interest was on assessing the potential of DMSI imagery to accurately map individual wine rows using advanced object-oriented classification techniques. Grapevines, are typical of row crops in that vigour is expressed not only as canopy density, but also in canopy dimensions. Information on the spatial extent of the canopy enables vineyard managers to determine where they should apply specific management techniques that maximise final production. In recent years yield maps produced by grape yield monitoring in Australia have shown that up to eight fold differences in yield can occur within a single vineyard block. Thus methodologies for accurately mapping the extent and variability of wine rows using non invasive remote sensing techniques are of paramount importance for better management strategies. Two aspects of remote sensing, namely spatial and spectral resolution greatly influence the accuracy of vine rows mapping. Previous researches suggest the use of imagery with a pixel resolution of up to 3 m. The sensors spectral resolution has important properties such as the position in the spectrum, the bandwith and the number of spectral bands, as these combined determine the extent to which individual targets (e.g. bare soil, vines, inter-row vegetation) can be discriminated.


The use of an object oriented approach accounting for the targets structure, shape, size and spectral characteristics enabled accurate mapping of wine rows. Within eCognition, raw DMSI bands and spectral indices were segmented, and subsequently classified using a fuzzy classification technique.


The results of this research show a moderately weak relationship between the imagery and crop yield data. Correlations between vegetation indices, raw and data ad yield were below 0.4. Regressions were calculated using multiple regression techniques, incorporating imagery acquired early and late in the growing season. Only one regression equation was capable of describing nearly a quarter of the possible variation present. The paper discusses external influences not considered during the research that may have driven these low correlation results.


Good results were obtained in the mapping of wine rows using an fuzzy object oriented classification of DMSI imagery. An overall kappa index of 0.76 is reported, with a producers accuracy of 91% and users accuracy of 86% in the identification of wine rows.


The evidences gathered through this research suggest that high spatial resolution air- (e.g. DMSI) or space-borne multispectral imagery (e.g. Quickbird, Ikonos, OrbView-3) offer great potential to produce base information for precision viticulture management, though the use of such imagery for dry agricultural crop yield prediction offered unsatisfactory results.