A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions.

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.

The development of a PCR-based marker linked to resistance to the blackcurrant gall mite (Cecidophyopsis ribis Acari: Eriophyidae).

Gall mite (Cecidophyopsis ribis) is the most serious pest of blackcurrant (Ribes nigrum L.), causing the damaging condition known as 'big bud' and also transmitting blackcurrant reversion virus (BRV) within and between plantations. The identification of resistant germplasm is at present a time-consuming and expensive process, dependent on field infestation plots. Resistance based on gene Ce introgressed from gooseberry has been used in UK breeding programmes for blackcurrant. Using a bulked segregant analysis, 90 AFLP primer combinations were screened and a linkage map constructed around the resistance locus controlled by Ce. Sixteen of the primer combinations produced a fragment in the resistant bulked progeny and the gall mite-resistant parent, but not in the susceptible bulked progeny and parent; subsequent testing on individual progeny identified an AFLP fragment closely linked to gall mite resistance. This fragment, designated E41M88-280, was converted to a PCR-based marker based on sequence-specific primers, amplifying only in resistant individuals. Validation of this marker across a range of susceptible and resistant blackcurrant germplasm with different genetic backgrounds confirmed its reliability in the identification of mite-resistant germplasm containing gene Ce. The conversion of an AFLP fragment to a sequence-based PCR marker simplifies its application and therefore increases its utility for selection of mite-resistant germplasm in high-throughput breeding programmes for blackcurrant.