Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status

High-throughput phenotyping of buckwheat (Fagopyrum esculentum Moench.) genotypes under water stress: exploring drought resistance for sustainable agriculture

BACKGROUND

As global agriculture faces the challenge of climate change, characterized by longer and more severe drought episodes, there is an increasing need for crop diversification and improved plant breeding. Buckwheat is one of the climate-resilient candidates for future important crops with remarkable adaptability to various biotic and abiotic stresses. As an underbred crop, a large number of genotypes should be assessed for the breeding of superior plants. Therefore, this study investigates the response of various buckwheat genotypes to water stress by high-throughput phenotyping and auxiliary plant physiology measurements.

RESULTS

We assessed six buckwheat genotypes from different regions under mild and severe water stress, focusing on morphological and physiological changes to understand drought tolerance mechanisms. Our findings revealed that reallocation of assimilated carbon from growth to secondary metabolite production is a common response to drought stress. Among the genotypes tested, Panda emerged as the most drought-resistant, with its morphology remaining the most stable under mild water stress and its ability to rapidly accumulate protective pigments in response to drought. Silver Hull also demonstrated resilience, maintaining its aboveground biomass under mild water stress at levels comparable to the control group. Additionally, the response magnitude to drought stress was linked to the biomass production potential of the genotypes, which was higher for those from warmer regions (Bhutan, Zimbabwe) and lower for those from colder regions (Poland, Canada).

CONCLUSION

The diversity in genotypic responses highlights the significant role of genetic variability in shaping drought resistance strategies in buckwheat. This research not only enhances our understanding of buckwheat's physiological responses to water stress but also holds promise for developing drought-resistant buckwheat varieties. These advancements are crucial for promoting sustainable agriculture in the face of climate change.

Evaluation of wheat drought resistance using hyperspectral and chlorophyll fluorescence imaging

Photosynthesis drives crop growth and production, and strongly affects grain yields; therefore, it is an ideal trait for wheat drought resistance breeding. However, studies of the negative effects of drought stress on wheat photosynthesis rates have lacked accurate evaluation methods, as well as high-throughput techniques. We investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data. We analyzed various morpho-physiological traits involved in wheat drought tolerance, including tiller number, leaf relative water content, and malondialdehyde content, to determine the relationships between drought resistance and hyperspectral and ChlF data. The results showed that the spectral first derivative ratio (FDR) between drought stress and control conditions in the 680-760 nm region was closely related to photosynthetic capacity and drought tolerance and that hyperspectral imaging can be used to monitor ChlF parameters, with bands sensitive to ChlF identified in two spectral regions (539-764 nm and 832-989 nm). The spectral first derivative at 989 nm had the strongest linear relationship with the minimal fluorescence (R2 = 0.49). An uninformative variable elimination algorithm indicated that FDRs in the green (504-609 nm), red (724-751 nm), and near-infrared (944-946 nm) light regions had great potential as indices of drought resistance. A support vector machine model based on the FDRs of these characteristic bands identified wheat drought resistance with 97.33% accuracy. These findings provide insight into the application of high-throughput technologies in studying drought resistance and photosynthesis in wheat.

Unveiling the mechanism of micro-and-nano plastic phytotoxicity on terrestrial plants: A comprehensive review of omics approaches

Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration. Recent advancements in omics technologies such as proteomics, metabolomics, transcriptomics, and microbiomics, coupled with emerging technologies like 4D omics, phenomics, spatial transcriptomics, and single-cell omics, offer unprecedented insight into the physiological, molecular, and cellular responses of terrestrial plants to MNPs exposure. This literature review synthesizes current findings regarding MNPs-induced phytotoxicity, emphasizing alterations in gene expression, protein synthesis, metabolic pathways, and physiological disruptions as revealed through omics analyses. We summarize how MNPs interact with plant cellular structures, disrupt metabolic processes, and induce oxidative stress, ultimately affecting plant growth and productivity. Furthermore, we have identified critical knowledge gaps and proposed future research directions, highlighting the necessity for integrative omics studies to elucidate the complex pathways of MNPs toxicity in terrestrial plants. In conclusion, this review underscores the potential of omics approaches to elucidate the mechanisms of MNPs-phytotoxicity and to develop strategies for mitigating the environmental impact of MNPs on plant health.

Hyperspectral imaging of liverwort Marchantia polymorpha identifies MpWRKY10 as a key regulator defining Foliar pigmentation patterns

Foliar pigmentation patterns vary among plant species and growth conditions. In this study, we utilize hyperspectral imaging to assess foliar pigmentation in the bryophyte Marchantia polymorpha under nutrient stress and identify associated genetic factors. Using singular value decomposition (SVD) for feature selection, we quantitate color variations induced by deficiencies in phosphate, nitrate, magnesium, calcium, and iron. Pseudo-colored thallus images show that disrupting MpWRKY10 causes irregular pigmentation with auronidin accumulation. Transcriptomic profiling shows that MpWRKY10 regulates phenylpropanoid pathway enzymes and R2R3-MYB transcription factors during phosphate deficiency, with MpMYB14 upregulation preceding pigment accumulation. MpWRKY10 is downregulated in older, pigmented thalli under phosphate deficiency but maintained in young thalli, where it suppresses pigmentation genes. This downregulation is absent in pigmented thalli due to aging. Comparative transcriptome analysis suggests similar WRKY and MYB roles in nutrient response and pigmentation in red-leaf lettuce, alluding to conserved genetic factors controlling foliar pigmentation patterns under nutrient deficiency.

Computer vision-based plants phenotyping: A comprehensive survey

The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

P-MIRU, a Polarized Multispectral Imaging System, Reveals Reflection Information on the Biological Surface

Reflection light forms the core of our visual perception of the world. We can obtain vast information by examining reflection light from biological surfaces, including pigment composition and distribution, tissue structure and surface microstructure. However, because of the limitations in our visual system, the complete information in reflection light, which we term 'reflectome', cannot be fully exploited. For example, we may miss reflection light information outside our visible wavelengths. In addition, unlike insects, we have virtually no sensitivity to light polarization. We can detect non-chromatic information lurking in reflection light only with appropriate devices. Although previous studies have designed and developed systems for specialized uses supporting our visual systems, we still do not have a versatile, rapid, convenient and affordable system for analyzing broad aspects of reflection from biological surfaces. To overcome this situation, we developed P-MIRU, a novel multispectral and polarization imaging system for reflecting light from biological surfaces. The hardware and software of P-MIRU are open source and customizable and thus can be applied for virtually any research on biological surfaces. Furthermore, P-MIRU is a user-friendly system for biologists with no specialized programming or engineering knowledge. P-MIRU successfully visualized multispectral reflection in visible/non-visible wavelengths and simultaneously detected various surface phenotypes of spectral polarization. The P-MIRU system extends our visual ability and unveils information on biological surfaces.

Evaluating potential of leaf reflectance spectra to monitor plant genetic variation

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation-information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.

Evaluation of thermoregulation of different pine organs in early spring and estimation of heat reward for the western conifer seed bug (Leptoglossus occidentalis) on male cones

The western conifer seed bug (WCSB, Leptoglossus occidentalis) is a pest of many pine species and is invasive worldwide. WCSB directly and indirectly deteriorates pine nut production by sucking seeds from cones. Currently, researchers think that WCSBs search for food by a combination of cues from visible light, infrared radiation, and chemicals such as monoterpenes. Some research revealed that WCSBs prefer larger cones, and it was thought that WCSBs suck seeds from and obtain more heat on larger cones. However, in early spring, we observed that most WCSBs gathered on male cones rather than on female cones and young cones. We hypothesized that male pine cones were warmer than female cones and needles, and WCSBs sucking male cones may receive more heat. To test these hypotheses, we measured spectral reflectance with a hyperspectral sensor and temperature of pine organs with tiny thermocouples, and the data were analyzed by a heat budget model. Our results revealed that male cones were significantly warmer and more reflective than female cones and needles, which may attract WCSBs. These results supported our hypothesis that WCSBs on male cones were warmer than those on other organs. This study will help further understanding of WCSBs and the adaptive value of pine cone colors.

Multi-Omics Techniques for Soybean Molecular Breeding

Soybean is a major crop that provides essential protein and oil for food and feed. Since its origin in China over 5000 years ago, soybean has spread throughout the world, becoming the second most important vegetable oil crop and the primary source of plant protein for global consumption. From early domestication and artificial selection through hybridization and ultimately molecular breeding, the history of soybean breeding parallels major advances in plant science throughout the centuries. Now, rapid progress in plant omics is ushering in a new era of precision design breeding, exemplified by the engineering of elite soybean varieties with specific oil compositions to meet various end-use targets. The assembly of soybean reference genomes, made possible by the development of genome sequencing technology and bioinformatics over the past 20 years, was a great step forward in soybean research. It facilitated advances in soybean transcriptomics, proteomics, metabolomics, and phenomics, all of which paved the way for an integrated approach to molecular breeding in soybean. In this review, we summarize the latest progress in omics research, highlight novel findings made possible by omics techniques, note current drawbacks and areas for further research, and suggest that an efficient multi-omics approach may accelerate soybean breeding in the future. This review will be of interest not only to soybean breeders but also to researchers interested in the use of cutting-edge omics technologies for crop research and improvement.

On-line monitoring of plant water status: Validation of a novel sensor based on photon attenuation of radiation through the leaf

Non-destructive real-time monitoring of leaf water status is important for precision irrigation practice to increase water productivity and reduce its use. To this end, we tested and validated a novel leaf sensor (Leaf Water Meter, LWM), based on the photon attenuation during the passage of the light through the leaf, to monitor plant water status. Four woody species were subjected to multiple cycles of dehydration and re-hydration, and the signals recorded by the LWM were compared with classical measurements of plant water relations (relative water content and water potential). A good agreement between the signals recorded by LWM sensor and the destructive measurements, throughout the repeated water stress and rewatering cycles, was found across all species. These results demonstrate that LWM sensor is a sensitive, non-destructive and easy-to-handle device to reliably monitor in continuous fashion leaf water status. In conclusion, this sensor may be considered a promising tool for smart irrigation scheduling in precision agriculture context to decrease water wastage in light of global change and increasing conflicts over water demand.

Measurement of Environmentally Influenced Variations in Anthocyanin Accumulations in Brassica rapa subsp. Chinensis (Bok Choy) Using Hyperspectral Imaging

Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in "purple magic" and "red stem" by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in "red stem," "white stem," and "green stem." Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.

Rapid metabolite response in leaf blade and petiole as a marker for shade avoidance syndrome

BACKGROUND

Shade avoidance syndrome (SAS) commonly occurs in plants experiencing vegetative shade, causing morphological and physiological changes that are detrimental to plant health and consequently crop yield. As the effects of SAS on plants are irreversible, early detection of SAS in plants is critical for sustainable agriculture. However, conventional methods to assess SAS are restricted to observing for morphological changes and checking the expression of shade-induced genes after homogenization of plant tissues, which makes it difficult to detect SAS early.

RESULTS

Using the model plant Arabidopsis thaliana, we introduced the use of Raman spectroscopy to measure shade-induced changes of metabolites in vivo. Raman spectroscopy detected a decrease in carotenoid contents in leaf blades and petioles of plants with SAS, which were induced by low Red:Far-red light ratio or high density conditions. Moreover, by measuring the carotenoid Raman peaks, we were able to show that the reduction in carotenoid content under shade was mediated by phytochrome signaling. Carotenoid Raman peaks showed more remarkable response to SAS in petioles than leaf blades of plants, which greatly corresponded to their morphological response under shade or high plant density. Most importantly, carotenoid content decreased shortly after shade induction but before the occurrence of visible morphological changes. We demonstrated this finding to be similar in other plant species. Comprehensive testing of Brassica vegetables showed that carotenoid content decreased during SAS, in both shade and high density conditions. Likewise, carotenoid content responded quickly to shade, in a manner similar to Arabidopsis plants.

CONCLUSIONS

In various plant species tested in this study, quantification of carotenoid Raman peaks correlate to the severity of SAS. Moreover, short-term exposure to shade can induce the carotenoid Raman peaks to decrease. These findings highlight the carotenoid Raman peaks as a biomarker for early diagnosis of SAS in plants.

Assessment of hyperspectral indicators related to the content of phenolic compounds and multispectral fluorescence records in chicory leaves exposed to various light environments

Hyperspectral analysis represents a powerful technique for diagnostics of morphological and chemical information from aboveground parts of the plants, but the real potential of the method in pre-screening of phenolics in leaves is still insufficiently explored. In this study, assessment of the sensitivity and reliability of non-invasive methods of various phenolic compounds, also analyzed by HPLC in chicory plants (Cichorium intybus L.) exposed to various color light pretreatments was done. The hyperspectral records in visible and near infrared (VNIR) spectra were recorded using a handheld spectrometer and relationships between the specific hyperspectral parameters and the contents of tested phenolic compounds in chicory leaves were analyzed. Moreover, the correlations between the hyperspectral parameters and related parameters derived from the multispectral fluorescence records were assessed to compare the sensitivity of both techniques. The results indicated a relatively high correlation of anthocyanin-related parameters (ARI, mARI, mACI indices) with the content of some of tested phenolic compounds (quercetin-3-gluconuride, isorhamnetine-3-gluconuride, etc.), as well as with fluorescence ANTH index. Similar trends were observed in flavonoid parameter based on the near infra-red spectral bands (700, 760 nm), which expressed a high correlation with chlorogenic acid. On the other hand, the most frequently used flavonoid (FLAVI) indices based on UV-to-blue band reflectance showed very weak correlations with phenolic compounds, as well as with fluorescence FLAV index. The detailed analysis of the correlation between reflectance and fluorescence flavonoid parameters has shown that the parameters based on spectral reflectance are sensitive to increase of UV-absorbing compounds from low to moderate values, but, unlike the fluorescence parameter, they are not useful to recognize a further increase from middle to high or very high contents. Thus, our results outlined the possibilities, but also the limits of the use of hyperspectral analysis for rapid screening phenolic content, providing a practical evidence towards more efficient production of bioactive compounds for pharmaceutical or nutraceutical use.

Phenotyping in Arabidopsis and Crops-Are We Addressing the Same Traits? A Case Study in Tomato

The convenient model Arabidopsis thaliana has allowed tremendous advances in plant genetics and physiology, in spite of only being a weed. It has also unveiled the main molecular networks governing, among others, abiotic stress responses. Through the use of the latest genomic tools, Arabidopsis research is nowadays being translated to agronomically interesting crop models such as tomato, but at a lagging pace. Knowledge transfer has been hindered by invariable differences in plant architecture and behaviour, as well as the divergent direct objectives of research in Arabidopsis versus crops compromise transferability. In this sense, phenotype translation is still a very complex matter. Here, we point out the challenges of "translational phenotyping" in the case study of drought stress phenotyping in Arabidopsis and tomato. After briefly defining and describing drought stress and survival strategies, we compare drought stress protocols and phenotyping techniques most commonly used in the two species, and discuss their potential to gain insights, which are truly transferable between species. This review is intended to be a starting point for discussion about translational phenotyping approaches among plant scientists, and provides a useful compendium of methods and techniques used in modern phenotyping for this specific plant pair as a case study.

In Vivo Spectral Distortions of Infrared Luminescent Nanothermometers Compromise Their Reliability

Luminescence nanothermometry has emerged over the past decade as an exciting field of research due to its potential applications where conventional methods have demonstrated to be ineffective. Preclinical research has been one of the areas that have benefited the most from the innovations proposed in the field. Nevertheless, certain questions concerning the reliability of the technique under in vivo conditions have been continuously overlooked by most of the scientific community. In this proof-of-concept, hyperspectral in vivo imaging is used to explain how unverified assumptions about the thermal dependence of the optical transmittance of biological tissues in the so-called biological windows can lead to erroneous measurements of temperature. Furthermore, the natural steps that should be taken in the future for a reliable in vivo luminescence nanothermometry are discussed together with a perspective view of the field after the findings here reported.

Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms

Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco, Vc,max ) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration, Jmax ) to alleviate this bottleneck. However, its performance varies significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO2 concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean R 2 value of the six regression techniques for predicting Vc,max (Jmax ) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean RMSE value varying from 47.1 (40.1) to 54.0 (44.7) μmol m-2 s-1. Regression stacking for Vc,max (Jmax ) performed better than the individual regression techniques with increases in R 2 of 0.1 (0.08) and decreases in RMSE by 4.1 (6.6) μmol m-2 s-1, equal to 8% (15%) reduction in RMSE. Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression technique to other plant phenotypic traits.

A low-cost and open-source platform for automated imaging

BACKGROUND

Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana.

RESULTS

A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers.

CONCLUSIONS

HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.

Measuring plant colors

Plant colors such as 'green leaf' and 'red apple' are often described based on human sense, even in scientific papers. On the other hand, colors are measured based on colorimetric principles in some papers, especially in the studies of horticultural plants. The science of color measurements ('colorimetry') is not included in any of the popular lectures in schools and universities, thus the principles of color measurements would not be understood by most researchers. The present review will overview the principles of colorimetry, and will introduce colorimetric methods which can be used for scientific measurement of plant colors. That is to say, the reflection spectrum of visible light (380-780 nm) is measured at 5-nm intervals on the surface of leaves or petals in 'Spectrometric Color Measurement' (SCM). The spectral data is multiplied with RGB or XYZ color matching functions and integrated to obtain RGB or XYZ intensities. Alternatively, approximate RGB values are directly obtained in 'Photographic Color Measurement' (PCM). RGB/XYZ intensities are further calculated to obtain 'hue', 'saturation', and 'lightness', the three factors of colors. Colorimetric insights into genetic regulations (such as MYB gene) and physiological regulations (such as alexandrite effect) of plant colors are also described.

Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits

Plant resistance to generalist pathogens with broad host ranges, such as Botrytis cinerea (Botrytis), is typically quantitative and highly polygenic. Recent studies have begun to elucidate the molecular genetic basis of plant-pathogen interactions using commonly measured traits, including lesion size and/or pathogen biomass. However, with the advent of digital imaging and high-throughput phenomics, there are a large number of additional traits available to study quantitative resistance. In this study, we used high-throughput digital imaging analysis to investigate previously poorly characterized visual traits of plant-pathogen interactions related to disease resistance using the Arabidopsis (Arabidopsis thaliana)/Botrytis pathosystem. From a large collection of visual lesion trait measurements, we focused on color, shape, and size to test how these aspects of the Arabidopsis/Botrytis interaction are genetically related. Through genome-wide association mapping in Arabidopsis, we show that lesion color and shape are genetically separable traits associated with plant disease resistance. Moreover, by employing defined mutants in 23 candidate genes identified from the genome-wide association mapping, we demonstrate links between loci and each of the different plant-pathogen interaction traits. These results expand our understanding of the functional mechanisms driving plant disease resistance.

An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice

With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680-760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.

Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification.

A Journey Through a Leaf: Phenomics Analysis of Leaf Growth in Arabidopsis thaliana

In Arabidopsis, leaves contribute to the largest part of the aboveground biomass. In these organs, light is captured and converted into chemical energy, which plants use to grow and complete their life cycle. Leaves emerge as a small pool of cells at the vegetative shoot apical meristem and develop into planar, complex organs through different interconnected cellular events. Over the last decade, numerous phenotyping techniques have been developed to visualize and quantify leaf size and growth, leading to the identification of numerous genes that contribute to the final size of leaves. In this review, we will start at the Arabidopsis rosette level and gradually zoom in from a macroscopic view on leaf growth to a microscopic and molecular view. Along this journey, we describe different techniques that have been key to identify important events during leaf development and discuss approaches that will further help unraveling the complex cellular and molecular mechanisms that underlie leaf growth.

Determination of Seed Soundness in Conifers Cryptomeria japonica and Chamaecyparis obtusa Using Narrow-Multiband Spectral Imaging in the Short-Wavelength Infrared Range

Regeneration of planted forests of Cryptomeria japonica (sugi) and Chamaecyparis obtuse (hinoki) is the pressing importance to the forest administration in Japan. Low seed germination rate of these species, however, has hampered low-cost production of their seedlings for reforestation. The primary cause of the low germinability has been attributed to highly frequent formation of anatomically unsound seeds, which are indistinguishable from sound germinable seeds by visible observation and other common criteria such as size and weight. To establish a method for sound seed selection in these species, hyperspectral imaging technique was used to identify a wavelength range where reflectance spectra differ clearly between sound and unsound seeds. In sound seeds of both species, reflectance in a narrow waveband centered at 1,730 nm, corresponding to a lipid absorption band in the short-wavelength infrared (SWIR) range, was greatly depressed relative to that in adjacent wavebands on either side. Such depression was absent or less prominent in unsound seeds. Based on these observations, a reflectance index SQI, abbreviated for seed quality index, was formulated using reflectance at three narrow SWIR wavebands so that it represents the extent of the depression. SQI calculated from seed area-averaged reflectance spectra and spatial distribution patterns of pixelwise SQI within each seed area were both proven as reliable criteria for sound seed selection. Enrichment of sound seeds was accompanied by an increase in germination rate of the seed lot. Thus, the methods described are readily applicable toward low-cost seedling production in combination with single seed sowing technology.

HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging

BACKGROUND

Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

RESULTS

The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content.

CONCLUSIONS

The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.

A review of imaging techniques for plant phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

The phenotype of grape leaves caused by acetochlor or fluoroglycofen, and effects of latter herbicide on grape leaves

Fluoroglycofen and acetochlor are two different herbicides used in vineyards to eradicate weeds. This present study first characterized the effects of these chemicals on phenotype of grape leaves. Results showed that acetochlor caused the middle- and upper-node grape leaves become yellow at 60th day after treatment, while fluoroglycofen caused the ones became dark green. Then the effects of fluoroglycofen on photosynthetic pigments and chloroplast ultrastructure were characterized. Results showed that fluoroglycofen increased the chlorophyll and carotenoid contents by different extent in different node leaves, while it did not affect the net photosynthesis rate significantly. Chloroplast ultrastructure analysis showed that the gap between thylakoids layers in few chloroplasts of middle-node leaves increased, which was also observed in ones of upper-node leaves; the number and size of chloroplast increased. Analysis on the deformed leaves of grapevines treated with 375 g ai ha(-1) fluoroglycofen showed that the starch grain per cell was much more and larger than that in the same size control leaves; the dark green and yellow parts had more or fewer chloroplast than the control, respectively, but both with more grana per chloroplast and less layers per granum. Chloroplasts went larger and round. Taken together, these results suggested that fluoroglycofen caused the grape leaves become dark green, which might be associated with the changes of chloroplast; the growth inhibition in the second year might be due to accumulation of starch.

Phenotyping and beyond: modelling the relationships between traits

Plant phenotyping technology has become more advanced with the capacity to measure many morphological and physiological traits on a given individual. With increasing automation, getting access to various traits on a high number of genotypes over time raises the need to develop systems for data storage and analyses, all congregating into plant phenotyping pipelines. In this review, we highlight several studies that illustrate the latest advances in plant multi-trait phenotyping and discuss future needs to ensure the best use of all these quantitative data. We assert that the next challenge is to disentangle how plant traits are embedded in networks of dependencies (and independencies) by modelling the relationships between them and how these are affected by genetics and environment.

Cell to whole-plant phenotyping: the best is yet to come

Imaging and image processing have revolutionized plant phenotyping and are now a major tool for phenotypic trait measurement. Here we review plant phenotyping systems by examining three important characteristics: throughput, dimensionality, and resolution. First, whole-plant phenotyping systems are highlighted together with advances in automation that enable significant throughput increases. Organ and cellular level phenotyping and its tools, often operating at a lower throughput, are then discussed as a means to obtain high-dimensional phenotypic data at elevated spatial and temporal resolution. The significance of recent developments in sensor technologies that give access to plant morphology and physiology-related traits is shown. Overall, attention is focused on spatial and temporal resolution because these are crucial aspects of imaging procedures in plant phenotyping systems.

Future scenarios for plant phenotyping

With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.