Field phenomics:will it enable crop improvement?

Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders’ needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders

Association mapping and genetic dissection of drought-induced canopy temperature differences in rice

Drought-stressed plants display reduced stomatal conductance, which results in increased leaf temperature by limiting transpiration. In this study, thermal imaging was used to quantify the differences in canopy temperature under drought in a rice diversity panel consisting of 293 indica accessions. The population was grown under paddy field conditions and drought stress was imposed for 2 weeks at flowering. The canopy temperature of the accessions during stress negatively correlated with grain yield (r= –0.48) and positively with plant height (r=0.56). Temperature values were used to perform a genome-wide association (GWA) analysis using a 45K single nucleotide polynmorphism (SNP) map. A quantitative trait locus (QTL) for canopy temperature under drought was detected on chromosome 3 and fine-mapped using a high-density imputed SNP map. The candidate genes underlying the QTL point towards differences in the regulation of guard cell solute intake for stomatal opening as the possible source of temperature variation. Genetic variation for the significant markers of the QTL was present only within the tall, low-yielding landraces adapted to drought-prone environments. The absence of variation in the shorter genotypes, which showed lower leaf temperature and higher grain yield, suggests that breeding for high grain yield in rice under paddy conditions has reduced genetic variation for stomatal response under drought

Platelet count and transfusion requirements during moderate or severe postpartum haemorrhage

Limited data exist on platelet transfusion during postpartum haemorrhage. We retrospectively analysed a consecutive cohort from a single centre of 347 women with moderate or severe postpartum haemorrhage, transfused according to national guidelines. Twelve (3%) women required a platelet transfusion. There were no differences between women who did and did not receive platelets with respect to age, mode of initiation of labour or mode of delivery. Women receiving a platelet transfusion had a lower median (IQR [range]) platelet count at study entry than women who did not receive platelets before haemorrhage (135 (97–175 [26–259])×109.l−1 vs 224 (186–274 [91–1006])×109.l−1), respectively), and at diagnosis of postpartum haemorrhage (median 114 (78–153 [58–238])×109.l−1 vs 193 (155–243 [78–762])×109.l−1 respectively). Six women were thrombocytopenic pre-delivery. The cause of haemorrhage that was associated with the highest rate of platelet transfusion was placental abruption, with three of 14 women being transfused. If antenatal thrombocytopenia or consumptive coagulopathy were not present, platelets were only required for haemorrhage > 5000 ml. Early formulaic platelet transfusion would have resulted in many women receiving platelets unnecessarily. Using current guidelines, the need for platelet transfusion is uncommon without antenatal thrombocytopenia, consumptive coagulopathy or haemorrhage > 5000 ml. We found no evidence to support early fixed-ratio platelet transfusion

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

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