Hemispherical photography has received renewed interest to describe plant canopies and measure canopy gap fraction from which important biophysical variables such as leaf area index (LAI) can be derived. This kind of remote sensing imagery is typically processed by setting a threshold on the histogram of a given image feature to segment the image and separate target from non-target pixels. Selecting such a threshold can be complicated due to varying image acquisition conditions and to the difficulty of defining canopy gaps. Having an operator who individually analyses images can be prohibitively time consuming for some applications, such as validating LAI products retrieved from satellite remote sensing where large numbers of samples are necessary. This paper presents how object-based image analysis can be applied to digital hemispherical photography in order to estimate automatically biophysical variables in a batch mode using the dedicated software CAN-EYE. The method is demonstrated by applying it to 114 sets of images obtained over 30 maize fields visited at several dates along the 2009 crop growing in Belgium and the Netherlands. The results obtained by the automatic method are comparable to those obtained by manual processing using CAN-EYE and this holds for DHPs acquired at different maize growth stages and with different viewing configurations. These encouraging results indicate object-based segmentation approach has great potential to provide efficient and automated solutions for hemispherical photography.