Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears

Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak

Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat

Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium-infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium-infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (Fv/Fm) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of Fv/Fm values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management

Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.

Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images

Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm-900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants' early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSR_b), and the red-edge chlorophyll index (CI_REDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning

Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions.

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described.

Comparative Performances of Beneficial Microorganisms on the Induction of Durum Wheat Tolerance to Fusarium Head Blight

Durum wheat production is seriously threatened by Fusarium head blight (FHB) attacks in Tunisia, and the seed coating by bio-agents is a great alternative for chemical disease control. This study focuses on evaluating, under field conditions, the effect of seed coating with Trichoderma harzianum, Meyerozyma guilliermondii and their combination on (i) FHB severity, durum wheat grain yield and TKW in three crop seasons, and (ii) on physiological parameters and the carbon and nitrogen content and isotope composition in leaves and grains of durum wheat. The results indicated that the treatments were effective in reducing FHB severity by 30 to 70% and increasing grain yield with an increased rate ranging from 25 to 68%, compared to the inoculated control. The impact of treatments on grain yield improvement was associated with higher NDVI and chlorophyll content and lower canopy temperature. Furthermore, the treatments mitigated the FHB adverse effects on N and C metabolism by resulting in a higher δ¹³Cgrain (¹³C/¹²Cgrain) and δ¹⁵Ngrain (¹⁵N/¹⁴Ngrain). Overall, the combination outperformed the other seed treatments by producing the highest grain yield and TKW. The high potency of seed coating with the combination suggests that the two microorganisms have synergetic or complementary impacts on wheat.

Genetic determinants of micronutrient traits in graminaceous crops to combat hidden hunger

KEY MESSAGE

Improving the nutritional content of graminaceous crops is imperative to ensure nutritional security, wherein omics approaches play pivotal roles in dissecting this complex trait and contributing to trait improvement. Micronutrients regulate the metabolic processes to ensure the normal functioning of the biological system in all living organisms. Micronutrient deficiency, thereby, can be detrimental that can result in serious health issues. Grains of graminaceous crops serve as an important source of micronutrients to the human population; however, the rise in hidden hunger and malnutrition indicates an insufficiency in meeting the nutritional requirements. Improving the elemental composition and nutritional value of the graminaceous crops using conventional and biotechnological approaches is imperative to address this issue. Identifying the genetic determinants underlying the micronutrient biosynthesis and accumulation is the first step toward achieving this goal. Genetic and genomic dissection of this complex trait has been accomplished in major cereals, and several genes, alleles, and QTLs underlying grain micronutrient content were identified and characterized. However, no comprehensive study has been reported on minor cereals such as small millets, which are rich in micronutrients and other bioactive compounds. A comparative narrative on the reports available in major and minor Graminaceae species will illustrate the knowledge gained from studying the micronutrient traits in major cereals and provides a roadmap for dissecting this trait in other minor species, including millets. In this context, this review explains the progress made in studying micronutrient traits in major cereals and millets using omics approaches. Moreover, it provides insights into deploying integrated omics approaches and strategies for genetic improvement in micronutrient traits in graminaceous crops.

Tackling microbial threats in agriculture with integrative imaging and computational approaches

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.

Breeding, Genetics, and Genomics Approaches for Improving Fusarium Wilt Resistance in Major Grain Legumes

Fusarium wilt (FW) disease is the key constraint to grain legume production worldwide. The projected climate change is likely to exacerbate the current scenario. Of the various plant protection measures, genetic improvement of the disease resistance of crop cultivars remains the most economic, straightforward and environmental-friendly option to mitigate the risk. We begin with a brief recap of the classical genetic efforts that provided first insights into the genetic determinants controlling plant response to different races of FW pathogen in grain legumes. Subsequent technological breakthroughs like sequencing technologies have enhanced our understanding of the genetic basis of both plant resistance and pathogenicity. We present noteworthy examples of targeted improvement of plant resistance using genomics-assisted approaches. In parallel, modern functional genomic tools like RNA-seq are playing a greater role in illuminating the various aspects of plant-pathogen interaction. Further, proteomics and metabolomics have also been leveraged in recent years to reveal molecular players and various signaling pathways and complex networks participating in host-pathogen interaction. Finally, we present a perspective on the challenges and limitations of high-throughput phenotyping and emerging breeding approaches to expeditiously develop FW-resistant cultivars under the changing climate.

Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images

Wheat yellow rust is one of the most destructive diseases in wheat production and significantly affects wheat quality and yield. Accurate and non-destructive identification of yellow rust is critical to wheat production management. Hyperspectral imaging technology has proven to be effective in identifying plant diseases. We investigated the feasibility of identifying yellow rust on wheat leaves using spectral features and textural features (TFs) of hyperspectral images. First, the hyperspectral images were preprocessed, and healthy and yellow rust-infected samples were obtained by creating regions of interest. Second, the extraction of spectral reflectance characteristics and vegetation indices (VIs) were performed from the preprocessed hyperspectral images, and the TFs were extracted using the grey-level co-occurrence matrix from the images transformed by principal component analysis. Third, the successive projections algorithm was employed to choose the optimum wavebands (OWs), and correlation-based feature selection was employed to select the optimal VIs and TFs (those most sensitive to yellow rust and having minimal redundancy between features). Finally, identification models of wheat yellow rust were established using a support vector machine and different features. Six OWs (538, 598, 689, 702, 751, and 895 nm), four VIs (nitrogen reflectance index, photochemical reflectance index, greenness index, and anthocyanin reflectance index), and four TFs (correlation 1, correlation 2, entropy 2, and second moment 3) were selected. The identification models based on the OWs, VIs, and TFs provided overall accuracies of 83.3%, 89.5%, and 86.5%, respectively. The TF results were especially encouraging. The models with the combination of spectral features and TFs exhibited better performance than those using the spectral features or TFs alone. The accuracies of the models with the combined features (OWs and TFs, Vis, and TFs) were 90.6% and 95.8%, respectively. These values were 7.3% and 6.3% higher, respectively, than those of the models using only the OWs or VIs. The model with the combined feature (VIs and TFs) had the highest accuracy (95.8%) and was used to map the yellow rust lesions on wheat leaves with different damage levels. The results showed that the yellow rust lesions on the leaves could be identified accurately. Overall, the combination of spectral features and TFs of hyperspectral images significantly improved the identification accuracy of wheat yellow rust.

Sweet Cherry Skin Colour Measurement as an Non-Destructive Indicator of Fruit Maturity

Colour measurement is one of the methods used to evaluate food quality. Aim of completed research was the evaluation of a fast and non-destructive method which consisted of assessing colour changes. It was used to determine the ripeness of cherries during their harvest. Additionally, the most significant parameter determining fruit ripeness was identified. Colour measurements of the Burlat cherry on the PHL A rootstocks were supposed to provide standards for practical evaluation of fruit ripeness of this species at an orchard. During the research, the measurements concerned the internal quality of the cherry fruit (firmness, extract content) and the force required to tear off the stem, depending on the size of the fruit. The extract appeared to be the most important indicator to be used for the determination of an optimum harvesting period. It was most prominently correlated with the cherry’s colour. Changes in the skin colour were the most reflected by the value of the parameter CIE a*. The coordinates CIE L* and b* are also important for the determination of fruit quality. Burlat cherries achieve their optimum harvesting ripeness if the coordinate a* is within the range 30.0 to 0.0, the coordinate b* within 10.0 to 0.0 and the coordinate L* within 30.0 to 20.0, which corresponds to the extract value of 12-20%.

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R² between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R² values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R² values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging

Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.

Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

Plant Phenotyping Research Trends, a Science Mapping Approach

Modern plant phenotyping, often using non-invasive technologies and digital technologies, is an emerging science and provides essential information on how genetics, epigenetics, environmental pressures, and crop management (farming) can guide selection toward productive plants suitable for their environment. Thus, phenotyping is at the forefront of future plant breeding. Bibliometric science mapping is a quantitative method that analyzes scientific publications throughout the terms present in their title, abstract, and keywords. The aim of this mapping exercise is to observe trends and identify research opportunities. This allows us to analyze the evolution of phenotyping research and to predict emerging topics of this discipline. A total of 1,827 scientific publications fitted our search method over the last 20 years. During the period 1997-2006, the total number of publications was only around 6.1%. The number of publications increased more steeply after 2010, boosted by the overcoming of technological bias and by a set of key developments at hard and software level (image analysis and data storage management, automation and robotics). Cluster analysis evidenced three main groups linked to genetics, physiology, and imaging. Mainly the model plant "Arabidopsis thaliana" and the crops "rice" and "triticum" species were investigated in the literature. The last two species were studied when addressing "plant breeding," and "genomic selection." However, currently the trend goes toward a higher diversity of phenotyped crops and research in the field. The application of plant phenotyping in the field is still under rapid development and this application has strong linkages with precision agriculture. EU co-authors were involved in 41.8% of the analyzed papers, followed by USA (15.4%), Australia (6.0%), and India (5.6%). Within the EU, coauthors were mainly affiliated in Germany (35.8%), France (23.7%), and United Kingdom (18.4%). Time seems right for new opportunities to incentivize research on more crops, in real field conditions, and to spread knowledge toward more countries, including emerging economies. Science mapping offers the possibility to get insights into a wide amount of bibliographic information, making them more manageable, attractive, and easy to serve science policy makers, stakeholders, and research managers.

Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize

Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed.

Use of multicolour fluorescence imaging for diagnosis of bacterial and fungal infection on zucchini by implementing machine learning

Zucchini (Cucurbita pepo L.) is a cucurbitaceous plant ranking high in economic importance among vegetable crops worldwide. Pathogen infections cause alterations in plants primary and secondary metabolism that lead to a significant decrease in crop quality and yield. Such changes can be monitored by remote and proximal sensing, providing spatial and temporal information about the infection process. Remote sensing can also provide specific signatures of disease that could be used in phenotyping and to detect a pest, forecast its evolution and predict crop yield. In this work, metabolic changes triggered by soft rot (caused by Dickeya dadantii) and powdery mildew (caused by Podosphaera fusca) on zucchini leaves have been studied by multicolour fluorescence imaging and by thermography. The fluorescence parameter F520/F680 showed statistically significant differences between infected (with D. dadantii or P. fusca) and mock-control leaves during the whole period of study. Artificial neural networks, logistic regression analyses and support vector machines trained with a set of features characterising the histograms of F520/F680 images could be used as classifiers, discriminating between healthy and infected leaves. These results show the applicability of multicolour fluorescence imaging on plant phenotyping.

Quantitative Disease Resistance: Dissection and Adoption in Maize

Maize is the world's most produced crop, providing food, feed, and biofuel. Maize production is constantly threatened by the presence of devastating pathogens worldwide. Characterization of the genetic components underlying disease resistance is a major research area in maize which is highly relevant for resistance breeding programs. Quantitative disease resistance (QDR) is the type of resistance most widely used by maize breeders. The past decade has witnessed significant progress in fine-mapping and cloning of genes controlling QDR. The molecular mechanisms underlying QDR remain poorly understood and exploited. In this review we discuss recent advances in maize QDR research and strategy for resistance breeding.

Phenomic Approaches and Tools for Phytopathologists

Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.

Ascribing Functions to Genes: Journey Towards Genetic Improvement of Rice Via Functional Genomics

Rice, one of the most important cereal crops for mankind, feeds more than half the world population. Rice has been heralded as a model cereal owing to its small genome size, amenability to easy transformation, high synteny to other cereal crops and availability of complete genome sequence. Moreover, sequence wealth in rice is getting more refined and precise due to resequencing efforts. This humungous resource of sequence data has confronted research fraternity with a herculean challenge as well as an excellent opportunity to functionally validate expressed as well as regulatory portions of the genome. This will not only help us in understanding the genetic basis of plant architecture and physiology but would also steer us towards developing improved cultivars. No single technique can achieve such a mammoth task. Functional genomics through its diverse tools viz. loss and gain of function mutants, multifarious omics strategies like transcriptomics, proteomics, metabolomics and phenomics provide us with the necessary handle. A paradigm shift in technological advances in functional genomics strategies has been instrumental in generating considerable amount of information w.r.t functionality of rice genome. We now have several databases and online resources for functionally validated genes but despite that we are far from reaching the desired milestone of functionally characterizing each and every rice gene. There is an urgent need for a common platform, for information already available in rice, and collaborative efforts between researchers in a concerted manner as well as healthy public-private partnership, for genetic improvement of rice crop better able to handle the pressures of climate change and exponentially increasing population.

Determination of the Optimum Harvest Window for Apples Using the Non-Destructive Biospeckle Method

Determination of the optimum harvest window plays a key role in the agro-food chain as the quality of fruit depends on the right harvesting time and appropriate storage conditions during the postharvest period. Usually, indices based on destructive measurements are used for this purpose, like the De Jager Index (PFW-1), FARS index and the most popular Streif Index. In this study, we proposed a biospeckle method for the evaluation of the optimum harvest window (OHW) of the "Ligol" and "Szampion" apple cultivars. The experiment involved eight different maturity stages, of which four were followed by long cold storage and shelf life to assist the determination of the optimum harvest window. The biospeckle activity was studied in relation to standard quality attributes (firmness, acidity, starch, soluble solids content, Streif Index) and physiological parameters (respiration and ethylene emission) of both apple cultivars. Changes of biospeckle activity (BA) over time showed moderate relationships with biochemical changes during apple maturation and ripening. The harvest date suggested by the Streif Index and postharvest quality indicators matched with characteristic decrease in BA. The ability of biospeckle method to characterize the biological state of apples was confirmed by significant correlations of BA with firmness, starch index, total soluble solids and Streif Index, as well as good match with changes in carbon dioxide and ethylene emission. However, it should be noted that correlations between variables changing over time are not as meaningful as independent observations. Also, it is a well-known property of the Pearson's correlation that its value is highly susceptible to outlier data. Due to its non-selective nature the BA reflected only the current biological state of the fruit and could be affected by many other factors. The investigations showed that the optimum harvest window for apples was indicated by the characteristic drop of BA during pre-harvest development. Despite this, at the current state of development the BA method cannot be used as an indicator alone. Due to rather poor results for prediction in OHW the BA measurements should be supported by other destructive methods to compensate its low selectivity.

Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification

The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares–discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets

BACKGROUND

Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping.

RESULTS

We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler.

CONCLUSIONS

Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org freely accessible to users of the plant phenotyping community.

Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets

BACKGROUND

Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping.

RESULTS

We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler.

CONCLUSIONS

Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org freely accessible to users of the plant phenotyping community.

Image-based phenotyping of plant disease symptoms

Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.

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.

Recent developments in hyperspectral imaging for assessment of food quality and safety

Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed.

A novel system for spatial and temporal imaging of intrinsic plant water use efficiency

Instrumentation and methods for rapid screening and selection of plants with improved water use efficiency are essential to address current issues of global food and fuel security. A new imaging system that combines chlorophyll fluorescence and thermal imaging has been developed to generate images of assimilation rate (A), stomatal conductance (gs), and intrinsic water use efficiency (WUEi) from whole plants or leaves under controlled environmental conditions. This is the first demonstration of the production of images of WUEi and the first to determine images of g s from themography at the whole-plant scale. Data are presented illustrating the use of this system for rapidly and non-destructively screening plants for alterations in WUEi by comparing Arabidopsis thaliana mutants (OST1-1) that have altered WUEi driven by open stomata, with wild-type plants. This novel instrument not only provides the potential to monitor multiple plants simultaneously, but enables intra- and interspecies variation to be taken into account both spatially and temporally. The ability to measure A, gs, and WUEi progressively was developed to facilitate and encourage the development of new dynamic protocols. Images illustrating the instrument's dynamic capabilities are demonstrated by analysing plant responses to changing photosynthetic photon flux density (PPFD). Applications of this system will augment the research community's need for novel screening methods to identify rapidly novel lines, cultivars, or species with improved A and WUEi in order to meet the current demands on modern agriculture and food production.

High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis

BACKGROUND

In order to select for quantitative plant resistance to pathogens, high throughput approaches that can precisely quantify disease severity are needed. Automation and use of calibrated image analysis should provide more accurate, objective and faster analyses than visual assessments. In contrast to conventional visible imaging, chlorophyll fluorescence imaging is not sensitive to environmental light variations and provides single-channel images prone to a segmentation analysis by simple thresholding approaches. Among the various parameters used in chlorophyll fluorescence imaging, the maximum quantum yield of photosystem II photochemistry (Fv/Fm) is well adapted to phenotyping disease severity. Fv/Fm is an indicator of plant stress that displays a robust contrast between infected and healthy tissues. In the present paper, we aimed at the segmentation of Fv/Fm images to quantify disease severity.

RESULTS

Based on the Fv/Fm values of each pixel of the image, a thresholding approach was developed to delimit diseased areas. A first step consisted in setting up thresholds to reproduce visual observations by trained raters of symptoms caused by Xanthomonas fuscans subsp. fuscans (Xff) CFBP4834-R on Phaseolus vulgaris cv. Flavert. In order to develop a thresholding approach valuable on any cultivars or species, a second step was based on modeling pixel-wise Fv/Fm-distributions as mixtures of Gaussian distributions. Such a modeling may discriminate various stages of the symptom development but over-weights artifacts that can occur on mock-inoculated samples. Therefore, we developed a thresholding approach based on the probability of misclassification of a healthy pixel. Then, a clustering step is performed on the diseased areas to discriminate between various stages of alteration of plant tissues. Notably, the use of chlorophyll fluorescence imaging could detect pre-symptomatic area. The interest of this image analysis procedure for assessing the levels of quantitative resistance is illustrated with the quantitation of disease severity on five commercial varieties of bean inoculated with Xff CFBP4834-R.

CONCLUSIONS

In this paper, we describe an image analysis procedure for quantifying the leaf area impacted by the pathogen. In a perspective of high throughput phenotyping, the procedure was automated with the software R downloadable at http://www.r-project.org/. The R script is available at http://lisa.univ-angers.fr/PHENOTIC/telechargements.html.

Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies

The functional analysis of the rice genome has entered into a high-throughput stage, and a project named RICE2020 has been proposed to determine the function of every gene in the rice genome by the year 2020. However, as compared with the robustness of genetic techniques, the evaluation of rice phenotypic traits is still performed manually, and the process is subjective, inefficient, destructive and error-prone. To overcome these limitations and help rice phenomics more closely parallel rice genomics, reliable, automatic, multifunctional, and high-throughput phenotyping platforms should be developed. In this article, we discuss the key plant phenotyping technologies, particularly photonics-based technologies, and then introduce their current applications in rice (wheat or barley) phenomics. We also note the major challenges in rice phenomics and are confident that these reliable high-throughput phenotyping tools will give plant scientists new perspectives on the information encoded in the rice genome.

Advanced phenotyping offers opportunities for improved breeding of forage and turf species

BACKGROUND AND AIMS

Advanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources.

SCOPE

This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed.

CONCLUSIONS

Progress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystems.

Relation of biospeckle activity with quality attributes of apples

Biospeckle is nondestructive optical technique based on the analysis of variations of laser light scattered from biological samples. Biospeckle activity reflects the state of the investigated object. In this study the relation of biospeckle activity (BA) with firmness, soluble solids content (SSC), titratable acidity (TA) and starch content (SC) during the shelf life of seven apple cultivars was studied. The results showed that the quality attributes change significantly during storage. Significant and pronounced positive correlation between BA and SC was found. This result shows that degradation of starch granules, which could be stimulated to vibration by intracellular cyclosis, causes a lesser number of laser light scattering centers and results in smaller apparent biospeckle activity.