Leveraging high-throughput hyperspectral imaging technology to detect cadmium stress in two leafy green crops and accelerate soil remediation efforts

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

Dose dependent effect of nitrogen on the phyto extractability of Cd in metal contaminated soil using Wedelia trilobata

Cadmium (Cd) is one of the toxic heavy metal that negatively affect plant growth and compromise food safety for human consumption. Nitrogen (N) is an essential macronutrient for plant growth and development. It may enhance Cd tolerance of invasive plant species by maintaining biochemical and physiological characteristics during phytoextraction of Cd. A comparative study was conducted to evaluate the phenotypical and physiological responses of invasive W. trilobata and native W. chinensis under low Cd (10 µM) and high Cd (80 µM) stress, along with different N levels (i.e., normal 91.05 mg kg-1 and low 0.9105 mg kg-1). Under low-N and Cd stress, the growth of leaves, stem and roots in W. trilobata was significantly increased by 35-23%, 25-28%, and 35-35%, respectively, compared to W. chinensis. Wedelia trilobata exhibited heightened antioxidant activities of catalase and peroxidase were significantly increased under Cd stress to alleviate oxidative stress. Similarly, flavonoid content was significantly increased by 40-50% in W. trilobata to promote Cd tolerance via activation of the secondary metabolites. An adverse effect of Cd in the leaves of W. chinensis was further verified by a novel hyperspectral imaging technology in the form of normalized differential vegetation index (NDVI) and photochemical reflectance index (PRI) compared to W. trilobata. Additionally, W. trilobata increased the Cd tolerance by regulating Cd accumulation in the shoots and roots, bolstering its potential for phytoextraction potential. This study demonstrated that W. trilobata positively responds to Cd with enhanced growth and antioxidant capabilities, providing a new platform for phytoremediation in agricultural lands to protect the environment from heavy metals pollution.

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.

Image-Based High-Throughput Phenotyping in Horticultural Crops

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

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.

Nondestructive testing and visualization of compound heavy metals in lettuce leaves using fluorescence hyperspectral imaging

This study evaluated the feasibility of nondestructive testing and visualization of compound heavy metals (cadmium and lead) in lettuce leaves using fluorescence hyperspectral imaging. In addition, a method involving wavelet transform and stepwise regression (WT-SR) was proposed to perform dimensionality reduction of fluorescence spectral data. Fluorescent hyperspectral image acquisition and mathematical analysis were carried out on lettuce leaf samples processed with different compound heavy metal concentrations. The entire lettuce leaf sample was selected as a region of interest (ROI). Savitzky-Golay (SG) algorithm, multivariate scatter correction (MSC), standard normalized variable (SNV), first derivative (1st Der) and second derivative (2nd Der) were used to preprocess the ROI fluorescence spectra. Further, the successive projections algorithm (SPA), the competitive adaptive reweighted sampling (CARS), the iteratively retaining informative variables (IRIV) and variable iterative space shrinkage approach (VISSA), and the wavelet transform combined with stepwise regression (WT-SR) were used to reduce the dimension of spectral data. Finally, the multiple linear regression (MLR) algorithm was used to build the compound heavy metal content detection models. The results showed that the MLR models based on the feature data obtained by 1st Der-WT-SR achieved reasonable performance with Rp2 of 0.7905, RMSEP of 0.0269 mg/kg and RPD of 2.477 for Cd content under wavelet fifth layer decomposition, and with Rp2 of 0.8965, RMSEP of 0.0096 mg/kg and RPD of 3.211 for Pb content under wavelet first layer decomposition. The distribution maps of cadmium and lead contents in lettuce leaves were established using the optimal prediction models. The results further confirmed the great potential of fluorescence hyperspectral technology combined with optimization algorithm for the detection of compound heavy metals.

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.

Classifying cadmium contaminated leafy vegetables using hyperspectral imaging and machine learning

Cadmium (Cd) is a toxic element that can accumulate in edible plant tissues and negatively impact human health. Traditional Cd quantification methods are time-consuming, expensive, and generate a lot of toxic waste, slowing development of methods to reduce uptake. The objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants using kale (Brassica oleracea) and basil (Ocimum basilicum) as model crops. The experiments were conducted in an automated phenotyping facility where all environmental conditions except soil Cd concentration were kept constant. Cd concentrations were determined at harvest using traditional methods and used to train the ML models with data collected from the imaging sensor. Visible/near infrared (VNIR) images were also collected at harvest and processed to calculate reflectance at 473 bands between 400 to 998 nm. All reflectance spectra were subject to the feature selection algorithm ReliefF and Principal Component Analysis (PCA) to generate data and provide input to evaluate three ML classification models: artificial neural network (ANN), ensemble learning (EL), and support vector machine (SVM). Plants were categorized according to Cd concentrations higher or lower than the safety threshold of 0.2 mg kg^-1 Cd. Wavelengths with the highest ranks for Cd detection were between 519 and 574, and 692 and 732 nm, indicating that Cd content likely altered the plants' chlorophyll content and altered leaf internal structure. All models were able to sort the plants into groups, though the model with the best F1 score was the ANN for the validation subset that utilized reflectance from all wavelengths. This study demonstrates that HSI and ML are promising technologies for the fast and precise diagnosis of Cd in leafy green plants, though additional studies are needed to adapt this approach for more complex field environments.

Diversity and functional traits of indigenous soil microbial flora associated with salinity and heavy metal concentrations in agricultural fields within the Indus Basin region, Pakistan

Soil salinization and heavy metal (HM) contamination are major challenges facing agricultural systems worldwide. Determining how soil microbial communities respond to these stress factors and identifying individual phylotypes with potential to tolerate these conditions while promoting plant growth could help prevent negative impacts on crop productivity. This study used amplicon sequencing and several bioinformatic programs to characterize differences in the composition and potential functional capabilities of soil bacterial, fungal, and archaeal communities in five agricultural fields that varied in salinity and HM concentrations within the Indus basin region of Pakistan. The composition of bacteria with the potential to fix atmospheric nitrogen (N) and produce the enzyme 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase were also determined. Microbial communities were dominated by: Euryarchaeota (archaea), Actinobacteria, Proteobacteria, Planctomycetota, Firimicutes, Patescibacteria and Acidobacteria (bacteria), and Ascomycota (fungi), and all soils contained phylotypes capable of N-fixation and ACC-deaminase production. Salinity influenced bacterial, but not archaeal or fungal communities. Both salinity and HM altered the relative abundance of many phylotypes that could potentially promote or harm plant growth. These stress factors also appeared to influence the potential functional capabilities of the microbial communities, especially in their capacity to cycle phosphorous, produce siderophores, and act as symbiotrophs or pathotrophs. Results of this study confirm that farms in this region are at risk due to salinization and excessive levels of some toxic heavy metals, which could negatively impact crop and human health. Changes in soil microbial communities and their potential functional capabilities are also likely to affect several critical agroecosystem services related to nutrient cycling, pathogen suppression, and plant stress tolerance. Many potentially beneficial phylotypes were identified that appear to be salt and HM tolerant and could possibly be exploited to promote these services within this agroecosystem. Future efforts to isolate these phylotypes and determine whether they can indeed promote plant growth and/or carry out other important soil processes are recommended. At the same time, identifying ways to promote the abundance of these unique phylotypes either through modifying soil and crop management practices, or developing and applying them as inoculants, would be helpful for improving crop productivity in this region.

The soil pH and heavy metals revealed their impact on soil microbial community

Soil microbial community is the main indicator having a crucial role in the remediation of polluted soils. These microbes can alter soil pH, organic matter in soils (SOM), soil physic-chemical properties, and potential soil respiration rate via their enzymatic activities. Similarly, heavy metals also have a crucial role in soil enzymatic activities. For this purpose, a number of methods are studied to evaluate the impact of soil pH (a key factor in the formation of biogeographic microbial patterns in bacteria) on bacterial diversity. The effects of pH on microbial activity are glamorous but still unclear. Whereas, some studies also indicate that soil pH alone is not the single key player in the diversity of soil bacteria. Ecological stability is achieved in a pollution-free environment and pH value. The pH factor has a significant impact on the dynamics of microbes' communities. Here, we try to discuss factors that directly or indirectly affect soil pH and the impact of pH on microbial activity. It is also discussed the environmental factors that contribute to establishing a specific bacterial community structure that must be determined. From this, it can be concluded that the environmental impact on soil pH, reducing soil pH and interaction with this factor, and reducing the effect of soil pH on soil microbial community.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

Heavy metal (loid)s phytotoxicity in crops and its mitigation through seed priming technology

Unexpected bioaccumulation and biomagnification of heavy metal(loid)s (HMs) in the environment have become a predicament for all living organisms, including plants. The presence of these HMs in the plant system raised the level of reactive oxygen species (ROS) and remodeled several vital cellular biomolecules. These lead to several morphological, physiological, metabolic, and molecular aberrations in plants ranging from chlorosis of leaves to the lipid peroxidation of membranes, and degradation of proteins and nucleic acid including the modulation of the enzymatic system, which ultimately affects the plant growth and productivity. Plants are equipped with several mechanisms to counteract the HMs toxicity. Among them, seed priming (SP) technology has been widely tested with the use of several inorganic chemicals, plant growth regulators (PGRs), gasotransmitters, nanoparticles, living organisms, and plant leaf extracts. The use of these compounds has the potential to alleviate the HMs toxicity through the strengthening of the antioxidant defense system, generation of low molecular weight metallothionein's (MTs), and phytochelatins (PCs), and improving seedling vigor during early growth stages. This review presents an account of the sources, uptake and transport, and phytotoxic effects of HMs with special attention to different mechanism/s, occurring to mitigate the HMs toxicity in plants employing SP technology.Novelty statement: To the best of our knowledge, this review has delineated the consequences of HMs on the crucial plant processes, which ultimately affect plant growth and development. This review also compiled the up to dated information on phytotoxicity of HMs through the use of SP technology, this review discussed how different types of SP approaches help in diminishing the concentration HMs in plant systems. Also, we depicted mechanisms, represent how HMs transport and their actions on cellular levels, and emphasized, how diverse SP technology effectiveness in the mitigation of plants' phytotoxicity in unique ways.

Vertical fate of Cd in soil under phytoremediation by Indian mustard and tall fescue

Soil columns were designed to investigate the vertical migration of Cd in Indian mustard (IM) and tall fescue (TF). The TF biomass was greater than the IM biomass, and the Cd content in IM was higher in the shoots but lower in the roots than that in TF. Both IM and TF released N and absorbed P and K during outdoor growth, differing from the results of the previous experiment in which plants were grown in greenhouses. TF was more absorbent and had less upward attraction than IM. The IM soil was more favorable for Cd precipitation than was the TF soil. Leaching remained the dominant effect, with only 2.28-3.40% and 2.65-3.90% of Cd absorbed by IM and TF, respectively. The present study on the vertical migration of Cd provides new insights into the phytoremediation mechanisms of IM and TF. HIGHLIGHTSVertical migration rate of Cd in soil was calculated.Cd precipitation in IM soil was greater and more excellence than TF soil.TF was more absorbent and had less upward attraction than IM.Leaching remained the dominant effect with only small absorb.