High-Throughput Screening of Free Proline Content in Rice Leaf under Cadmium Stress Using Hyperspectral Imaging with Chemometrics

Vis-NIR spectra-image transformation based on circular spectral mapping for measurement of particulate matter concentration

BACKGROUND

The fine particulate matter (PM2.5), one of the most concerned airborne pollutants, significantly impacts air quality and human health. The potential hazard of PM2.5 related to its concentration, while the traditional methods for PM concentration measuring were expensive, time-consuming, while low-cost sensors often suffer from poor accuracy and stability. Therefore, there is a great need for a rapid, precise and stable measurement method for filter-based PM2.5.

RESULTS

We propose a novel spectra-image transformation and fusion method for filter-based PM2.5 measurement using a portable visible near infrared (Vis-NIR) spectrometer. Traditional machine learning models based on spectra alone achieved low accuracy (R2p < 0.8). To improve performance, we introduced the circular spectral mapping (CSM) method to transform PM2.5 spectra into CSM images, which were processed using ResNet-18, ShuffleNet V2, and MobileNet V2 networks with an attention mechanism module. The optimal model, ShuffleNetV2_Attn, improved R2p to 0.9935. To furtherly improve the model stability, the numerical and graphical feature fusions were conducted, and the ShuffleNetV2_Attn was selected as optimal feature extractor of CSM images. The machine learning models were built based on fusion features, and the optimal model was the partial least squares (PLS) model based on fusion features extracted by successive projections algorithm (SPA), of which the R2p, RMSEP and mean absolute percentage error (MAPEp) were 0.9947, 6.0213 μg/m3 and 4.17 %, demonstrating high accuracy and stability overall concentration range.

SIGNIFICANCE

The proposed spectra-image transformation and fusion method greatly improved the accuracy and efficiency of filter-based PM2.5 measurement. It overcome the limitations of spectral-based machine learning methods, which often fail to capture full-band characteristics, and provides a new approach for integrating numerical and graphical spectral information.

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging

Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with R_p² of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with R_p² of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with R_p² of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.

Proximal hyperspectral sensing of abiotic stresses in plants

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Microbiome-Driven Proline Biogenesis in Plants under Stress: Perspectives for Balanced Diet to Minimize Depression Disorders in Humans

According to the World Health Organization (WHO), depression is a leading cause of disability worldwide and a major contributor to the overall global burden of mental disorders. An increasing number of studies have revealed that among 20 different amino acids, high proline consumption is a dietary factor with the strongest impact on depression in humans and animals, including insects. Recent studies acknowledged that gut microbiota play a key role in proline-related pathophysiology of depression. In addition, the multi-omics approach has alleged that a high level of metabolite proline is directly linked to depression severity, while variations in levels of circulating proline are dependent on microbiome composition. The gut-brain axis proline analysis is a gut microbiome model of studying depression, highlighting the critical importance of diet, but nothing is known about the role of the plant microbiome-food axis in determining proline concentration in the diet and thus about preventing excessive proline intake through food consumption. In this paper, we discuss the protocooperative potential of a holistic study approach combining the microbiota-gut-brain axis with the microbiota-plant-food-diet axis, as both are involved in proline biogenesis and metabolism and thus on in its effect on mood and cognitive function. In preharvest agriculture, the main scientific focus must be directed towards plant symbiotic endophytes, as scavengers of abiotic stresses in plants and modulators of high proline concentration in crops/legumes/vegetables under climate change. It is also implied that postharvest agriculture-including industrial food processing-may be critical in designing a proline-balanced diet, especially if corroborated with microbiome-based preharvest agriculture, within a circular agrifood system. The microbiome is suggested as a target for selecting beneficial plant endophytes in aiming for a balanced dietary proline content, as it is involved in the physiology and energy metabolism of eukaryotic plant/human/animal/insect hosts, i.e., in core aspects of this amino acid network, while opening new venues for an efficient treatment of depression that can be adapted to vast groups of consumers and patients. In that regard, the use of artificial intelligence (AI) and molecular biomarkers combined with rapid and non-destructive imaging technologies were also discussed in the scope of enhancing integrative science outcomes, agricultural efficiencies, and diagnostic medical precisions.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.

Development of multi-disturbance bagging Extreme Learning Machine method for cadmium content prediction of rape leaf using hyperspectral imaging technology

Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp² of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.

Biochar Alleviates Phytotoxicity by Minimizing Bioavailability and Oxidative Stress in Foxtail Millet (Setaria italica L.) Cultivated in Cd- and Zn-Contaminated Soil

Soil contamination with multiple heavy metals is a global environmental issue that poses a serious threat to public health and ecological safety. Biochar passivation is an efficient and economical technology to prevent heavy metal contamination of Cd; however, its effects on compound-contaminated and weakly alkaline soil remain unclear. Further, the mechanisms mediating the immobilization effects of biochar have not been evaluated. In this study, three biochar treated at different pyrolytic temperatures [300°C (BC300), 400°C (BC400), and 500°C (BC500)] were applied to Cd-/Zn-contaminated soils, and their effects on plant growth, photosynthetic characteristics, Cd/Zn accumulation and distribution in foxtail millet were evaluated. Further, the effect of biochar application on the soil physicochemical characteristics, as well as the diversity and composition of the soil microbiota were investigated. Biochar significantly alleviated the phytotoxicity of Cd and Zn. DTPA (diethylenetriamine pentaacetic acid)-Cd and DTPA-Zn content was significantly reduced following biochar treatment via the transformation of exchangeable components to stable forms. BC500 had a lower DTPA-Cd content than BC300 and BC400 by 42.87% and 39.29%, respectively. The BC500 passivation ratio of Cd was significantly higher than that of Zn. Biochar application also promoted the growth of foxtail millet, alleviated oxidative stress, and reduced heavy metal bioaccumulation in shoots, and transport of Cd from the roots to the shoots in the foxtail millet. The plant height, stem diameter, biomass, and photosynthetic rates of the foxtail millet were the highest in BC500, whereas the Cd and Zn content in each organ and malondialdehyde and hydrogen peroxide content in the leaves were the lowest. Moreover, biochar application significantly increased the abundance of soil bacteria and fungi, as well as increasing the fungal species richness compared to no-biochar treatment. Overall, biochar was an effective agent for the remediation of heavy metal-contaminated soil. The passivation effect of biochar exerted on heavy metals in soil was affected by the biochar pyrolysis temperature, with BC500 showing the best passivation effect.