Article
Multi-Focus Fusion Technique on Low-Cost Camera
Images for Canola Phenotyping
Thang Cao
1
, Anh Dinh
1,
*, Khan A. Wahid
1
ID
, Karim Panjvani
1
and Sally Vail
2
1
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9,
Canada; thang.cao@usask.ca (T.C.); khan.wahid@usask.ca (K.A.W.); karim.panjvani@usask.ca (K.P.)
2
Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C5, Canada; Sally.Vail@agr.gc.ca
* Correspondence: anh.dinh@usask.ca; Tel.: +1-306-966-5344
Received: 19 March 2018; Accepted: 5 June 2018; Published: 8 June 2018
Abstract:
To meet the high demand for supporting and accelerating progress in the breeding of
novel traits, plant scientists and breeders have to measure a large number of plants and their
characteristics accurately. Imaging methodologies are being deployed to acquire data for quantitative
studies of complex traits. Images are not always good quality, in particular, they are obtained
from the field. Image fusion techniques can be helpful for plant breeders with more comfortable
access plant characteristics by improving the definition and resolution of color images. In this work,
the multi-focus images were loaded and then the similarity of visual saliency, gradient, and color
distortion were measured to obtain weight maps. The maps were refined by a modified guided
filter before the images were reconstructed. Canola images were obtained by a custom built mobile
platform for field phenotyping and were used for testing in public databases. The proposed method
was also tested against the five common image fusion methods in terms of quality and speed.
Experimental results show good re-constructed images subjectively and objectively performed by
the proposed technique. The findings contribute to a new multi-focus image fusion that exhibits
a competitive performance and outperforms some other state-of-the-art methods based on the visual
saliency maps and gradient domain fast guided filter. The proposed fusing technique can be extended
to other fields, such as remote sensing and medical image fusion applications.
Keywords: image fusion; multi-focus; weight maps; gradient domain; fast guided filter.
1. Introduction
The sharp increase in demand for global food raises the awareness of the public, especially
agricultural scientists, to global food security. To meet the high demand for food in 2050, agriculture
will need to produce almost 50 percent more food than was produced in 2012 [
1
]. There are many ways
to improve yields for canola and other crops. One of the solutions is to increase breeding efficiency.
In the past decade, advances in genetic technologies, such as next generation DNA sequencing, have
provided new methods to improve plant breeding techniques. However, the lack of knowledge of
phenotyping capabilities limits the ability to analyze the genetics of quantitative traits related to
plant growth, crop yield, and adaptation to stress [
2
]. Phenotyping creates opportunities not only
for functional research on genes, but also for the development of new crops with beneficial features.
Image-based phenotyping methods are those integrated approaches that enable the potential to greatly
enhance plant researchers’ ability to characterize many different traits of plants. Modern advanced
imaging methods provide high-resolution images and enable the visualization of multi-dimensional
data. The basics of image processing have been thoroughly studied and published. Readers can
find useful information on image fusion in the textbooks by Starck or Florack [
3
,
4
]. These methods
allow plant breeders and researchers to obtain exact data, speed up image analysis, bring high
Sensors 2018, 18, 1887; doi:10.3390/s18061887 www.mdpi.com/journal/sensors
