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Nitrogen (N) fertilizer maximizes the growth of oilseed rape (Brassica napus L.) by improving photosynthetic performance. Elucidating the dynamic relationship between fluorescence and plant N status could provide a non-destructive diagnosis of N status and the breeding of N-efficient cultivars. The aim of this study was to explore the impacts of different N treatments on photosynthesis at a spatial–temporal scale and to evaluate the performance of three fluorescence techniques for the diagnosis of N status. One-way ANOVA and linear discriminant analysis were applied to analyze fluorescence data acquired by a continuous excitation chlorophyll fluorimeter (OJIP transient analysis), pulse amplitude-modulated chlorophyll fluorescence (PAM-ChlF), and multicolor fluorescence (MCF) imaging. The results showed that the maximum quantum efficiency of PSII photochemistry (Fv/Fm) and performance index for photosynthesis (PIABS) of bottom leaves were sensitive to N status at the bolting stage, whereas the red fluorescence/far-red fluorescence ratio of top leaves was sensitive at the early seedling stage. Although the classification of N treatments by the three techniques achieved comparable accuracies, MCF imaging showed the best potential for early diagnosis of N status in field phenotyping because it had the highest sensitivity in the top leaves, at the early seedling stage. The findings of this study could facilitate research on N management and the breeding of N-efficient cultivars.

Background The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS. Results The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds. Conclusion This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies. Electronic supplementary material The online version of this article (10.1186/s13007-019-0432-x) contains supplementary material, which is available to authorized users.

Huanglongbing (HLB) is a highly destructive disease to citrus that is threatening the global citrus industry. It is a great challenge for HLB disease detection at various stages due to the long asymptomatic period and the similar symptom to nutrient deficient trees. This research was aimed to propose an effective method for HLB detection in different seasons and cultivars based on hyperspectral imaging coupled with carbohydrate metabolism analysis. It was found that sucrose accumulated more steadily than starch, glucose and fructose in infected leaves through the hot and cool seasons, but nutrient (Fe) deficient leaves presented a reverse pattern to HLB infected leaves. Spectral and textural features from optimal wavelengths and principle component images were well linked to the HLB fingerprint. The three-class classification models for healthy, HLB infected (asymptomatic and symptomatic), and nutrient deficient leaves achieved 90.2%, 96.0%, and 92.6% accuracy for the cool season, hot season, and the whole period, respectively, using least squares-support vector machine (LS-SVM) classifier. Additionally, the robustness of classification model was validated by a different citrus cultivar using model transfer strategy with the overall accuracy of 93.5%. These results demonstrated the potential of hyperspectral imaging combined with carbohydrate metabolic analysis for HLB detection in different seasons and cultivars.

White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall accuracy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and 13C concentration. This agrees with the fact that there is more 13C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments.

Huanglongbing (HLB) is one of the most destructive diseases of citrus, which has posed a serious threat to the global citrus production. This research was aimed to explore the use of chlorophyll fluorescence imaging combined with feature selection to characterize and detect the HLB disease. Chlorophyll fluorescence images of citrus leaf samples were measured by an in-house chlorophyll fluorescence imaging system. The commonly used chlorophyll fluorescence parameters provided the first screening of HLB disease. To further explore the photosynthetic fingerprint of HLB infected leaves, three feature selection methods combined with the supervised classifiers were employed to identify the unique fluorescence signature of HLB and perform the three-class classification (i.e., healthy, HLB infected, and nutrient deficient leaves). Unlike the commonly used fluorescence parameters, this novel data-driven approach by using the combination of the mean fluorescence parameters and image features gave the best classification performance with the accuracy of 97%, and presented a better interpretation for the spatial heterogeneity of photochemical and non-photochemical components in HLB infected citrus leaves. These results imply the potential of the proposed approach for the citrus HLB disease diagnosis, and also provide a valuable insight for the photosynthetic response to the HLB disease.