Search

Hand-held chlorophyll meters or leaf-clip-type sensors indirectly and instantaneously measure leaf N content. They can provide an N nutrition index (NNI) value that is crucial information for adjusting the amount of N fertilizer to the actual N status of the plant. Although these measurements are non-invasive and non-destructive, they require numerous repetitions at the canopy scale. The objective of this work was to explore the potential of visible images to predict nitrogen status in winter wheat crops from estimating NNI and to compare these results with those deduced from classical methods. Based on a dark green colour index (DGCI), which combines hue, saturation and brightness, a normalized DGCI (nDGCI) was proposed as the ratio between the measurements of the study microplot and those of the over-fertilized microplot. The methodology was performed on winter wheat microplots with a nitrogen gradient. Half of the microplots were grown with a single cultivar (LG Absalon) and the other half with a mixture of four wheat cultivars. The impact of optical device (digital camera or smartphone), the white balance (Manual or Automatic), the crop growth stage (two-nodes or heading) and cultivars (single or mixed) on the relationship between (DGCI, nDGCI) and NNI was evaluated. The results showed a close correlation between the nDGCI values and the NNI_NTester values, especially on a single cultivar (LG Absalon; R2 = 0.73 up to 0.91 with smartphone). It suggested that the relationship is highly sensitive to the wheat cultivar. This approach with no specific calibration of images is promising for the estimation of N requirements in wheat field. [ABSTRACT FROM AUTHOR]

Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly straindependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems. [ABSTRACT FROM AUTHOR]

Summary: Although mechanisms involved in response of Saccharomyces cerevisiae to osmotic challenge are well described for low and sudden stresses, little is known about how cells respond to a gradual increase of the osmotic pressure (reduced water activity; aw) over several generations as it could encounter during drying in nature or in food processes. Using glycerol as a stressor, we propagated S. cerevisiae through a ramp of the osmotic pressure (up to high molar concentrations to achieve testing‐to‐destruction) at the rate of 1.5 MPa day‐1 from 1.38 to 58.5 MPa (0.990–0.635 aw). Cultivability (measured at 1.38 MPa and at the harvest osmotic pressure) and glucose consumption compared with the corresponding sudden stress showed that yeasts were able to grow until about 10.5 MPa (0.926 aw) and to survive until about 58.5 MPa, whereas glucose consumption occurred until 13.5 MPa (about 0.915 aw). Nevertheless, the ramp conferred an advantage since yeasts harvested at 10.5 and 34.5 MPa (0.778 aw) showed a greater cultivability than glycerol‐shocked cells after a subsequent shock at 200 MPa (0.234 aw) for 2 days. FTIR analysis revealed structural changes in wall and proteins in the range 1.38–10.5 MPa, which would be likely to be involved in the resistance at extreme osmotic pressure. [ABSTRACT FROM AUTHOR]

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production ( δ BMc ). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development. [ABSTRACT FROM AUTHOR]

Abstract The aim of the study was to extract B-phycoerythrin from Porphyridium cruentum while preserving its structure. The high hydrostatic pressure treatments were chosen as extraction technology. Different methods have been used to observe the effects of the treatment: spectrophotometry and confocal laser scanning microscopy followed by image processing analysis. Image processing led to the generation of masks used for the identification of three clusters: intra, extra and intercellular. All methods showed that high hydrostatic pressure treatments between 50 and 500 MPa failed to extract B-phycoerythrin from Porphyridium cruentum cells. The fluorescence emission was negatively impacted by high hydrostatic pressure treatment from 400 MPa for the extracellular and intercellular cluster and from 500 MPa for the intercellular cluster. These results suggest that high pressure treatments could induce the denaturation of B-phycoerythrin in all clusters but with different intensities depending on the cluster. Graphical abstract Unlabelled Image Highlights • 5 min at 500 MPa treatment had no effect on cell morphology. • Extra and intercellular clusters showed fluorescence decrease from 400 MPa. • Intracellular cluster showed fluorescence decrease from 500 MPa. • Intracellular cluster may play a protective effect towards B-phycoerythrin. [ABSTRACT FROM AUTHOR]