Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

Combined analysis of transcriptomics with metabolomics provides insights into the resistance mechanism in winter jujube using L-Methionine

Black rots lead to great economic losses in winter jujube industry. The objective of this research was to delve into the underlying mechanisms of enhanced resistance of winter jujube fruit to black rot by L-Methionine (Met) treatment. The findings revealed that the application of Met significantly curtailed lesion diameter and decay incidence in winter jujube fruit. The peroxidase (POD) activity in the Met-treated jujubes was 3.06-fold that in the control jujubes after 4 d of treatment. By day 8, the activities of phenylalanine ammonia-lyase (PAL), chitinase (CHI) and β-1,3-glucanase (GLU) in the Met-treated jujubes had surged to their zenith, being 1.39, 1.22, and 1.52 times in the control group, respectively. At the end of storage, the flavonoid and total phenol content remained 1.58 and 1.06 times than that of the control group. Based on metabolomics and transcriptomics analysis, Met treatment upregulated 6 key differentially expressed metabolites (DEMs) (succinic acid, trans-ferulic acid, salicylic acid, delphinium pigments, (S)-abscisic acid, and hesperidin-7-neohesperidin), 12 key differentially expressed genes (DEGs) (PAL, CYP73A, COMT, 4CL, CAD, POD, UGT72E, ANS, CHS, IAA, TCH4 and PR1), which were involved in phenylpropanoid biosynthesis pathway, flavonoid biosynthesis pathway and plant hormone signal transduction pathway. Further analysis revealed that the most of the enzymes, DEMs and DEGs in this study were associated with both antioxidant and disease resistance. Consequently, Met treatment enhanced disease resistance of winter jujube fruit by elevating antioxidant capacity and triggering defense response. This study might provide theoretical support for utilizing Met in the management and prevention of post-harvest black rot in winter jujube.

Leaf litter from Cynanchum auriculatum Royle ex Wight leads to root rot outbreaks by Fusarium solani, hindering continuous cropping

Cynanchum auriculatum Royle ex Wight (CA) is experiencing challenges with continuous cropping obstacle (CCO) due to soil-borne fungal pathogens. The leaf litter from CA is regularly incorporated into the soil after root harvesting, but the impact of this practice on pathogen outbreaks remains uncertain. In this study, a fungal strain D1, identified as Fusarium solani, was isolated and confirmed as a potential factor in CCO. Both leave extract (LE) and root extract (RE) were found to inhibit seed germination and the activities of plant defense-related enzymes. The combinations of extracts and D1 exacerbated these negative effects. Beyond promoting the proliferation of D1 in soil, the extracts also enhanced the hypha weight, spore number, and spore germination rate of D1. Compared to RE, LE exhibited a greater degree of promotion in the activities of pathogenesis-related enzymes in D1. Additionally, caffeic acid and ferulic acid were identified as potential active compounds. LE, particularly in combination with D1, induced a shift in the composition of fungal communities rather than bacterial communities. These findings indicate that the water extract of leaf litter stimulated the growth and proliferation of fungal strain D1, thereby augmenting its pathogenicity toward CA and ultimately contributing to the CCO process.

A deep learning model for rapid classification of tea coal disease

BACKGROUND

The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification.

RESULTS

Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging.

CONCLUSIONS

This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

Low temperature response index for monitoring freezing injury of tea plant

Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R^{2 =} 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants.

Transcriptomic and biochemical analyses of drought response mechanism in mung bean (Vignaradiata (L.) Wilczek) leaves

Drought is a major factor that limiting mung bean development. To clarify the molecular mechanism of mung bean in response to drought stress, 2 mung bean groups were established, the experimental group (drought-treated) and the control group (normal water management). With prominent difference of 2 groups in stomatal conductance, relative water content and phenotype, leaf samples were collected at 4 stages, and the physiological index of MDA, POD, chlorophyll, and soluble proteins were estimated. RNA-seq was used to obtain high quality data of samples, and differentially expressed genes were identified by DESeq2. With GO and KEGG analysis, DEGs were enriched into different classifications and pathways. WGCNA was used to detect the relationship between physiological traits and genes, and qPCR was performed to confirm the accuracy of the data. We obtained 169.49 Gb of clean data from 24 samples, and the Q30 of each date all exceeded 94%. In total, 8963 DEGs were identified at 4 stages between the control and treated samples, and the DEGs were involved in most biological processes. 1270 TFs screened from DEGs were clustered into 158 TF families, such as AP2, RLK-Pelle-DLSVA, and NAC TF families. Genes related to physiological traits were closely related to plant hormone signaling, carotenoid biosynthesis, chlorophyll metabolism, and protein processing. This paper provides a large amount of data for drought research in mung bean.

Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations

(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R² = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R² = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.

A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.

Detection of triterpene acids distribution in loquat (Eriobotrya japonica) leaf using hyperspectral imaging

Hyperspectral images (431-962nm) and partial least squares (PLS) were used to detect the distribution of triterpene acids within loquat (Eriobotrya japonica) leaves. 72 fresh loquat leaves in the young group, mature group and old group were collected for hyperspectral imaging; and triterpene acids content of the loquat leaves was analyzed using high performance liquid chromatography (HPLC). Then the spectral data of loquat leaf hyperspectral images and the triterpene acids content were employed to build calibration models. After spectra pre-processing and wavelength selection, an optimum calibration model (Rp=0.8473, RMSEP=2.61mg/g) for predicting triterpene acids was obtained by synergy interval partial least squares (siPLS). Finally, spectral data of each pixel in the loquat leaf hyperspectral image were extracted and substituted into the optimum calibration model to predict triterpene acids content of each pixel. Therefore, the distribution map of triterpene acids content was obtained. As shown in the distribution map, triterpene acids are accumulated mainly in the leaf mesophyll regions near the main veins, and triterpene acids concentration of young group is less than that of mature and old groups. This study showed that hyperspectral imaging is suitable to determine the distribution of active constituent content in medical herbs in a rapid and non-invasive manner.

A photo-stable fluorescent chiral thiourea probe for enantioselective discrimination of chiral guests

Herein, a chiral thiourea Schiff base derived from (1R,2R)-1,2-cyclohexanediamine and tetraphenylethylene (TPE) was applied as a highly effective chiral sensor for the enantioselective discrimination of various acids and aminesviaion-pair and hydrogen-bond interaction.

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.