Abstract
Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved.
Highlights
Plant breeders are constantly striving to improve crop productivity through the breeding of plants with desirable agronomic traits that are tolerant of abiotic stresses such as drought, mineral deficiency or heat stress and biotic stresses caused by pests and diseases
Breeders require tools for detecting the biochemical, physiological, and developmental responses to such stresses. These ‘phenotyping’ tools need to be capable of high-throughput screening of the huge number of plants that are typically generated and characterised as part of plant breeding programmes, whether for the mapping of quantitative trait loci (QTL) or for direct
A wide range of sensors is available for field use [10, 11]; these are most efficiently deployed on mobile field platforms [12, 13], while lighter sensors may be deployed on unmanned aerial vehicles (UAVs) [14,15,16]
Summary
Plant breeders are constantly striving to improve crop productivity through the breeding of plants with desirable agronomic traits that are tolerant of abiotic stresses such as drought, mineral deficiency or heat stress and biotic stresses caused by pests and diseases. Raspberry is a perennial crop species grown widely in Europe and North America, and raspberry production faces a number of specific challenges. Breeders require tools for detecting the biochemical, physiological, and developmental responses to such stresses. These ‘phenotyping’ tools need to be capable of high-throughput screening of the huge number of plants that are typically generated and characterised as part of plant breeding programmes, whether for the mapping of quantitative trait loci (QTL) or for direct. A wide range of sensors is available for field use [10, 11]; these are most efficiently deployed on mobile field platforms [12, 13], while lighter sensors may be deployed on unmanned aerial vehicles (UAVs) [14,15,16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
