Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints of sos Mutants to Drought Stress

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

Deep learning-empowered crop breeding: intelligent, efficient and promising

Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.

Drought stress detection technique for wheat crop using machine learning

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

High-throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy

The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline-alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline-alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the Q_A^-reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs.

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

Transcriptome analysis reveals the key pathways and candidate genes involved in salt stress responses in Cymbidium ensifolium leaves

BACKGROUND

Cymbidium ensifolium L. is known for its ornamental value and is frequently used in cosmetics. Information about the salt stress response of C. ensifolium is scarce. In this study, we reported the physiological and transcriptomic responses of C. ensifolium leaves under the influence of 100 mM NaCl stress for 48 (T48) and 96 (T96) hours.

RESULTS

Leaf Na⁺ content, activities of the antioxidant enzymes i.e., superoxide dismutase, glutathione S-transferase, and ascorbate peroxidase, and malondialdehyde content were increased in salt-stressed leaves of C. ensifolium. Transcriptome analysis revealed that a relatively high number of genes were differentially expressed in CKvsT48 (17,249) compared to CKvsT96 (5,376). Several genes related to salt stress sensing (calcium signaling, stomata closure, cell-wall remodeling, and ROS scavenging), ion balance (Na⁺ and H⁺), ion homeostasis (Na⁺/K⁺ ratios), and phytohormone signaling (abscisic acid and brassinosteroid) were differentially expressed in CKvsT48, CKvsT96, and T48vsT96. In general, the expression of genes enriched in these pathways was increased in T48 compared to CK while reduced in T96 compared to T48. Transcription factors (TFs) belonging to more than 70 families were differentially expressed; the major families of differentially expressed TFs included bHLH, NAC, MYB, WRKY, MYB-related, and C3H. A Myb-like gene (CenREV3) was further characterized by overexpressing it in Arabidopsis thaliana. CenREV3's expression was decreased with the prolongation of salt stress. As a result, the CenREV3-overexpression lines showed reduced root length, germination %, and survival % suggesting that this TF is a negative regulator of salt stress tolerance.

CONCLUSION

These results provide the basis for future studies to explore the salt stress response-related pathways in C. ensifolium.

Different pruning level effects on flowering period and chlorophyll fluorescence parameters of Loropetalum chinense var. rubrum

"Pruning" is a simple and efficient way to control the flowering period, but it is rarely used in perennial woody ornamental plants. In this paper, Loropetalum chinense var. rubrum was pruned in different degrees, and the relationship between pruning intensity and flowering number, and flowering time and chlorophyll fluorescence parameters were compared. After statistics, it was found that pruning could advance blossoms of L. chinense var. rubrum; also, light and heavy cutting could both obtain a larger number of flowers. In addition, through correlation analysis, it was found that during the flowering period, the Rfd parameter of the unpruned treatment had a very significant positive correlation with the number of flowers FN, which was 0.81. In other pruning treatment groups, Rfd and FN also presented a certain positive correlation, indicating that the Rfd parameter can be used to predict the number of flowers during the flowering process of L. chinense var. rubrum. The research results provided a new idea for the regulation of the flowering period of L. chinense var. rubrum and other woody ornamental plants and laid the foundation for the diversified application of L. chinense var. rubrum.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Stress Detection Using Proximal Sensing of Chlorophyll Fluorescence on the Canopy Level

Chlorophyll fluorescence is interesting for phenotyping applications as it is rich in biological information and can be measured remotely and non-destructively. There are several techniques for measuring and analysing this signal. However, the standard methods use rather extreme conditions, e.g., saturating light and dark adaption, which are difficult to accommodate in the field or in a greenhouse and, hence, limit their use for high-throughput phenotyping. In this article, we use a different approach, extracting plant health information from the dynamics of the chlorophyll fluorescence induced by a weak light excitation and no dark adaption, to classify plants as healthy or unhealthy. To evaluate the method, we scanned over a number of species (lettuce, lemon balm, tomato, basil, and strawberries) exposed to either abiotic stress (drought and salt) or biotic stress factors (root infection using Pythium ultimum and leaf infection using Powdery mildew Podosphaera aphanis). Our conclusions are that, for abiotic stress, the proposed method was very successful, while, for powdery mildew, a method with spatial resolution would be desirable due to the nature of the infection, i.e., point-wise spread. Pythium infection on the roots is not visually detectable in the same way as powdery mildew; however, it affects the whole plant, making the method an interesting option for Pythium detection. However, further research is necessary to determine the limit of infection needed to detect the stress with the proposed method.

Effect of Drought Stress on Chlorophyll Fluorescence Parameters, Phytochemical Contents, and Antioxidant Activities in Lettuce Seedlings

This study monitored changes in chlorophyll fluorescence (CF), growth parameters, soil moisture content, phytochemical content (proline, ascorbic acid, chlorophyll, total phenol content (TPC), and total flavonoid content (TFC)), and antioxidant activities in 12-day-old lettuce (Lactuca sativa L.) seedlings grown under drought stress (no irrigation) and control (well irrigated) treatments in controlled conditions for eight days. Measurements occurred at two-day intervals. Among ten CF parameters studied, effective quantum yield of photochemical energy conversion in PSII (Y(PSII)), coefficient of photochemical quenching (qP), and coefficient of photochemical quenching of variable fluorescence based on the lake model of PSII (qL) significantly decreased in drought-stressed seedlings from day 6 of treatment compared to control. In contrast, maximum quantum yield (Fv/Fm), ratio of fluorescence (Rfd), and quantum yield of non-regulated energy dissipation in PSII (Y(NO)) were significantly affected only at the end. All growth parameters decreased in drought-stressed seedlings compared to control. Proline started increasing from day 4 and showed ~660-fold elevation on day 8 compared to control. Chlorophyll, ascorbic acid, TPC, TFC, and antioxidant activities decreased in drought-stressed seedlings. Results showed major changes in all parameters in seedlings under prolonged drought stress. These findings clarify effects of drought stress in lettuce seedlings during progressive drought exposure and will be useful in the seedling industry.

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

Optimal temporal-spatial fluorescence techniques for phenotyping nitrogen status in oilseed rape

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

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.

Semi-Supervised Convolutional Neural Network for Law Advice Online

With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep neural network-based natural language processing (DNN-NLP) is one of the most promising approaches to implement text classification. Meanwhile, as the convolutional neural network-based (CNN-based) methods developed, CNN-based text classification has already achieved impressive results. However, previous work applied amounts of manually-annotated data, which increased the labor cost and reduced the adaptability of the approach. Hence, we present a new semi-supervised model to solve the problem of data annotation. Our method learns the embedding of small text regions from unlabeled data and then integrates the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model are used as an additional input to the CNN’s convolution layer. In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance. The proposed method is evaluated experimentally subject to a law case description dataset and English standard dataset RCV1 . On Chinese data, the simulation results demonstrate that, compared with the existing methods such as linear SVM, our scheme respectively improves by 7.76%, 7.86%, 9.19%, and 2.96% the precision, recall, F-1, and Hamming loss. Analogously, the results suggest that compared to CNN, our scheme respectively improves by 4.46%, 5.76%, 5.14% and 0.87% in terms of precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized in other real-world applications such as news classification and public opinion monitoring.