OCEAN COLOUR ANALYSIS USING CZCS DATA

A low-cost image processor has been designed and built to provide a system suitable for investigating the quantitative mapping of phytoplankton patchiness using Nimbus-7 CZCS satellite images. The processor design was based upon a Motorola 68000 computer linked to a 768x512x8 bit imagestore. High resolution images were input from the CZCS CCT via a 40 Mbyte tape transport. The system had the novel capability for real time linear and non-linear operations upon images on a pixel-by-pixel basis and fast evaluation (<10 seconds) of retrieval algorithms involving narrow band images. Software was developed to manage the images in the following ways: high emphasis filtering, edge detection, contrast stretch routines, rectification, and pseudo colour routines for grey-level colouring prior to display of the processed image on a double resolution colour monitor. Initial testing of the instrument was via multiband aerial photographs input through a broadcast quality camera, although the major analysis was carried out on CZCS data. Software was developed to correct the measured radiances at the satellite for atmospheric effects, thus giving values of the water-leaving radiances. In the correlation studies the sea truth was obtained in the form of chlorophyll concentrations determined during UOR surveys of the English Channel. Both high and low chlorophyll concentration scenes were analysed. The algorithm testing involved ratioing the radiances from the various narrow bands and incorporating them in quantitative expressions which were linear, logarithmic and polynomial. Four different images were investigated and the results showed that the observed chlorophyll concentrations were best correlated with water-leaving radiances through a linear expression. The spectral Information was also analysed with a clustering-technique to identify patches of chlorophyll of varying concentrations. The work shows that digital image processing can be used in conjunction with retrieval algorithms to provide real time detection of phytoplankton fronts in coastal waters .Institute for Marine Environmental Research, Prospect Place, Plymouth, Devo

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Globally it has been estimated that only one third of applied N is recovered in the harvested component of grain crops (Raun and Johnson 1999). This represents an incredible waste of resource and the overuse has detrimental environmental and economic consequences. There is substantial variation in nutrient use efficiency (NUE) from region to region, between crops and in different cropping systems. As a consequence, both local and crop specific solutions will be required for NUE improvement at local as well as at national and international levels. Strategies to improve NUE will involve improvements to germplasm and optimized agronomy adapted to climate and location. Essential to effective solutions will be an understanding of genetics (G), environment (E) and management (M) and their interactions (G x E x M). To implement appropriate solutions will require agronomic management, attention to environmental factors and improved varieties, optimized for current and future climate scenarios. As NUE is a complex trait with many contributing processes, identifying the correct trait for improvement is not trivial. Key processes include nitrogen capture (uptake efficiency), utilization efficiency (closely related to yield), partitioning (harvest index: biochemical and organ-specific) and trade-offs between yield and quality aspects (grain nitrogen content), as well as interactions with capture and utilization of other nutrients. A long-term experiment, the Broadbalk experiment at Rothamsted, highlights many factors influencing yield and nitrogen utilization in wheat over the last 175 years, particularly management and yearly variation. A more recent series of trials conducted over the past 16 years has focused on separating the key physiological sub-traits of NUE, highlighting both genetic and seasonal variation. This perspective describes these two contrasting studies which indicate G x E x M interactions involved in nitrogen utilization and summarizes prospects for the future including the utilization of high throughput phenotyping technology

The significance of glucosinolates for sulfur storage in Brassicaceae seedlings

Brassica juncea seedlings contained a twofold higher glucosinolate content than B. rapa and these secondary sulfur compounds accounted for up to 30% of the organic sulfur fraction. The glucosinolate content was not affected by H2S and SO2 exposure, demonstrating that these sulfur compounds did not form a sink for excessive atmospheric supplied sulfur. Upon sulfate deprivation, the foliarly absorbed H2S and SO2 replaced sulfate as the sulfur source for growth of B. juncea and B. rapa seedlings. The glucosinolate content was decreased in sulfate-deprived plants, though its proportion of organic sulfur fraction was higher than that of sulfate-sufficient plants, both in absence and presence of H2S and SO2. The significance of myrosinase in the in situ turnover in these secondary sulfur compounds needs to be questioned, since there was no direct co-regulation between the content of glucosinolates and the transcript level and activity of myrosinase. Evidently, glucosinolates cannot be considered as sulfur storage compounds upon exposure to excessive atmospheric sulfur and are unlikely to be involved in the re-distribution of sulfur in B. juncea and B. rapa seedlings upon sulfate deprivation

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions

Feeding the world sustainably - efficient nitrogen use

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use

Overexpression of a NAC transcription factor delays leaf senescence and increases grain nitrogen concentration in wheat

Increasing the duration of leaf photosynthesis during grain filling using slow-senescing functional stay-green phenotypes is a possible route for increasing grain yields in wheat (Triticum aestivum L.). However, delayed senescence may negatively affect nutrient remobilisation and hence reduce grain protein concentrations and grain quality. A novel NAC1-type transcription factor (hereafter TaNAC-S) was identified in wheat, with gene expression located primarily in leaf/sheath tissues, which decreased during post-anthesis leaf senescence. Expression of TaNAC-S in the second leaf correlated with delayed senescence in two doubled-haploid lines of an Avalon 3 Cadenza population (lines 112 and 181), which were distinct for leaf senescence. Transgenic wheat plants overexpressing TaNAC-S resulted in delayed leaf senescence (stay-green phenotype). Grain yield, aboveground biomass, harvest index and total grain N content were unaffected, but NAC over-expressing lines had higher grain N concentrations at similar grain yields compared to non-transgenic controls. These results indicate that TaNAC-S is a negative regulator of leaf senescence, and that delayed leaf senescence may lead not only to increased grain yields but also to increased grain protein concentrations.D. Zhao, A. P. Derkx, D.-C. Liu, P. Buchner, M. J. Hawkesfor