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 genetic disorder known as ‘crumbly’ fruit is becoming a serious problem in the European raspberry industry. The study set out to examine the crumbly phenotype in a red raspberry mapping population under two environments (field and polytunnel) across six seasons in an effort to understand variability of the syndrome and to examine whether genetic factors were important and if so, whether QTL associated with the phenotype could be identified. This highlighted that seasonal, environmental (field or polytunnel) and genetic factors all influence the condition. Two QTL that are important for the genetic control of the condition have been located on linkage groups one and three, and an association with ripening time has been identified.

A comprehensive lexicon is a necessary communication tool between the panel leader and panelists to describe each sensory stimulus potentially evoked by a product. In the current scientific breeding and trading scenario, a multilingual sensory lexicon is necessary to ensure the consistency of sensory evaluations when tests are conducted across countries and/or with international panelists. This study aimed to develop a reference multilingual lexicon for raspberry (Rubus idaeus L.) and blueberry (Vaccinium corymbosum L.) to perform comparative sensory tests through panels operating in different countries using their native language. Attributes were collected from state-of-the-art literature and integrated with a detailed description of the sensory stimulus associated with each term. A panel of sensory judges was trained to test lexicon efficacy. After training, panelists evaluated three cultivars of blueberry and raspberry through RATA (Rate All That Apply), which allowed missing attributes to be excluded while rating those actually present. Results showed the discerning efficacy of the lexicon developed can be a valuable tool for planning sensory evaluations held in different countries, opening up further possibilities to enrich blueberry and raspberry descriptor lists with emerging terms from local experience and evaluations of berry genotypes with peculiar traits.

Fruit consumption in the UK, particularly of berry fruits, is expanding rapidly and consumer demand for blueberries (Vaccinium spp.) is at record levels with UK growers unable to meet current demand. Consumers are aware that eating fruit can be pleasurable and with appropriate packaging, convenient, but blueberry remains unfamiliar to a wide cross section of UK consumers. There is great potential for UK growers to supply high quality fresh blueberries across a wide season so that consumption of this healthy fruit can be integrated into the British diet. A genetic framework for future crop improvement is required to develop a thriving and sustainable industry. The genetic component of this project builds on the statistical developments derived from the software program, TetraploidMap, to identify fruit quality, health, and agronomic-related quantitative trait loci in tetraploid blueberry for marker assisted breeding.