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Reviews
Hamuda, E;Glavin, M;Jones, E
2016
July
A survey of image processing techniques for plant extraction and segmentation in the field
Published
1
Optional Fields
CORN ZEA-MAYS WEED-CONTROL CRITICAL PERIOD MACHINE VISION YIELD LOSS COLOR SEGMENTATION DIGITAL IMAGES RELATIVE-TIME CROP IDENTIFICATION
In this review, we present a comprehensive and critical survey on image-based plant segmentation techniques. In this context, "segmentation" refers to the process of classifying an image into plant and non plant pixels. Good performance in this process is crucial for further analysis of the plant such as plant classification (i.e. identifying the plant as either crop or weed), and effective action based on this analysis, e.g. precision application of herbicides in smart agriculture applications.The survey briefly discusses pre-processing of images, before focusing on segmentation. The segmentation stage involves the segmentation of plant against the background (identifying plant from a background of soil and other residues). Three primary plant extraction algorithms, namely, (i) colour index-based segmentation, (ii) threshold-based segmentation, (iii) learning-based segmentation are discussed. Based on its prevalence in the literature, this review focuses in particular on colour index-based approaches. Therefore, a detailed discussion of the segmentation performance of colour index-based approaches is presented, based on studies from the literature conducted in the recent past, particularly from 2008 to 2015. Finally, we identify the challenges and some opportunities for future developments in this space. (C) 2016 Elsevier B.V. All rights reserved.
OXFORD
ELSEVIER SCI LTD
0168-1699
184
199
10.1016/j.compag.2016.04.024
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