Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy

Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet the growing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged as promising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneous production, and reducing agro-waste remain substantial challenges in these production systems. One potential solution is the use of optical sensing technology, which can provide real-time data to help growers make informed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluate crop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematic literature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoor farming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 to identify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations and inspirations for possible new research directions. The results of this review will contribute to the development of sustainable and efficient methods of food production.

Influence of indole acetic acid and trehalose, with and without zinc oxide nanoparticles coated urea on tomato growth in nitrogen deficient soils

Nitrogen deficiency in low organic matter soils significantly reduces crop yield and plant health. The effects of foliar applications of indole acetic acid (IAA), trehalose (TA), and nanoparticles-coated urea (NPCU) on the growth and physiological attributes of tomatoes in nitrogen-deficient soil are not well documented in the literature. This study aims to explore the influence of IAA, TA, and NPCU on tomato plants in nitrogen-deficient soil. Treatments included control, 2mM IAA, 0.1% TA, and 2mM IAA + 0.1% TA, applied with and without NPCU. Results showed that 2mM IAA + 0.1% TA with NPCU significantly improved shoot length (~ 30%), root length (~ 63%), plant fresh (~ 48%) and dry weight (~ 48%), number of leaves (~ 38%), and leaf area (~ 58%) compared to control (NPCU only). Additionally, significant improvements in chlorophyll content, total protein, and total soluble sugar, along with a decrease in antioxidant activity (POD, SOD, CAT, and APX), validated the effectiveness of 2mM IAA + 0.1% TA with NPCU. The combined application of 2mM IAA + 0.1% TA with NPCU can be recommended as an effective strategy to enhance tomato growth and yield in nitrogen-deficient soils. This approach can be integrated into current agricultural practices to improve crop resilience and productivity, especially in regions with poor soil fertility. To confirm the efficacy of 2mM IAA + 0.1% TA with NPCU in various crops and climatic conditions, additional field studies are required.

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.

Contemporary applications of vibrational spectroscopy in plant stresses and phenotyping

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Harnessing Raman spectroscopy for the analysis of plant diversity

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (F e S O 4 ), zinc (II) sulphate (Z n S O 4 ), and copper (II) sulphate (C u S O 4 ), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Elucidation of sex from mature Palmer amaranth (Amaranthus palmeri) leaves using a portable Raman spectrometer

Palmer amaranth (Amaranthus palmeri) is a pervasive and troublesome weed species that poses significant challenges to agriculture in the United States. Identifying the sex of Palmer amaranth plants is crucial for developing tailored control measures due to the distinct characteristics and reproductive strategies exhibited by male and female plants. Traditional methods for sex determination are expensive and time-consuming, but recent advancements in spectroscopic techniques offer new possibilities. This study explores the potential of portable Raman spectroscopy for determining the sex of mature Palmer amaranth plants in-field. Raman analysis of the plant leaves reveals spectral differences associated with nitrate salts, lipids, carotenoids, and terpenoids, allowing for high accuracy and reliable identification of the plant's sex; male plants had higher concentrations of these compounds compared to females. It was also found that male plants had higher concentrations of these compounds compared to the females. Raman spectra were analyzed using a machine learning tool, partial least squares discriminant analysis (PLS-DA), to generate accuracies of no less than 83.7% when elucidating sex from acquired spectra. These findings provide insights into the sex-specific characteristics of Palmer amaranth and suggest that Raman analysis, combined with PLS-DA, can be a promising, non-destructive, and efficient method for sex determination in field settings. This approach has implications for developing sex-specific management strategies to monitor and control this invasive weed in real-world environments, benefiting farmers, agronomists, researchers, and master gardeners.

Effects of Storage Temperatures on Nitrogen Assimilation and Remobilization during Post-Harvest Senescence of Pak Choi

In the agricultural industry, the post-harvest leafy vegetable quality and shelf life significantly influence market value and consumer acceptability. This study examined the effects of different storage temperatures on leaf senescence, nitrogen assimilation, and remobilization in Pak Choi (Brassica rapa subsp. chinensis). Mature Pak Choi plants were harvested and stored at two different temperatures, 4 °C and 25 °C. Senescence was tracked via chlorophyll content and leaf yellowing. Concurrently, alterations in the total nitrogen, nitrate, and protein content were quantified on days 0, 3, 6, and 9 in old, mid, and young leaves of Pak Choi plants. As expected, 4 °C alleviated chlorophyll degradation and delayed senescence of Pak Choi compared to 25 °C. Total nitrogen and protein contents were inversely correlated, while the nitrate content remained nearly constant across leaf groups at 25 °C. Additionally, the transcript levels of genes involved in nitrogen assimilation and remobilization revealed key candidate genes that were differentially expressed between 4 °C and 25 °C, which might be targeted to extend the shelf life of the leafy vegetables. Thus, this study provides pivotal insights into the molecular and physiological responses of Pak Choi to post-harvest storage conditions.

Systemic movement of long non-coding RNA ELENA1 attenuates leaf senescence under nitrogen deficiency

Nitrogen is an essential macronutrient that is absorbed by roots and stored in leaves, mainly as ribulose-1,5-bisphosphate carboxylase/oxygenase1,2. During nitrogen deficiency (-N), plants activate leaf senescence for source-to-sink nitrogen remobilization for adaptative growth3-6. However, how -N signals perceived by roots are propagated to shoots remains underexplored. We found that ELF18-INDUCED LONG NONCODING RNA 1 (ELENA1) is -N inducible and attenuates -N-induced leaf senescence in Arabidopsis. Analysis of plants expressing the ELENA1 promoter β-glucuronidase fusion gene showed that ELENA1 is transcribed specifically in roots under -N. Reciprocal grafting of the wild type and elena1 demonstrated that ELENA1 functions systemically. ELENA1 dissociates the MEDIATOR SUBUNIT 19a-ORESARA1 transcriptional complex, thereby calibrating senescence progression. Our observations establish the systemic regulation of leaf senescence by a root-derived long non-coding RNA under -N in Arabidopsis.

Spectroscopic Analyses Highlight Plant Biostimulant Effects of Baker's Yeast Vinasse and Selenium on Cabbage through Foliar Fertilization

The main aim of this study is to find relevant analytic fingerprints for plants' structural characterization using spectroscopic techniques and thermogravimetric analyses (TGAs) as alternative methods, particularized on cabbage treated with selenium-baker's yeast vinasse formulation (Se-VF) included in a foliar fertilizer formula. The hypothesis investigated is that Se-VF will induce significant structural changes compared with the control, analytically confirming the biofortification of selenium-enriched cabbage as a nutritive vegetable, and particularly the plant biostimulant effects of the applied Se-VF formulation on cabbage grown in the field. The TGA evidenced a structural transformation of the molecular building blocks in the treated cabbage leaves. The ash residues increased after treatment, suggesting increased mineral accumulation in leaves. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) evidenced a pectin-Iα-cellulose structure of cabbage that correlated with each other in terms of leaf crystallinity. FTIR analysis suggested the accumulation of unesterified pectin and possibly (seleno) glucosinolates and an increased network of hydrogen bonds. The treatment with Se-VF formulation induced a significant increase in the soluble fibers of the inner leaves, accompanied by a decrease in the insoluble fibers. The ratio of soluble/insoluble fibers correlated with the crystallinity determined by XRD and with the FTIR data. The employed analytic techniques can find practical applications as fast methods in studies of the effects of new agrotechnical practices, while in our particular case study, they revealed effects specific to plant biostimulants of the Se-VF formulation treatment: enhanced mineral utilization and improved quality traits.

Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

The utilization of microbes for sustainable food production

As the global human population continues to grow, the demand for food rises accordingly. Unfortunately, anthropogenic activities, climate change, and the release of gases from the utilization of synthetic fertilizers and pesticides are causing detrimental effects on sustainable food production and agroecosystems. Despite these challenges, there remain underutilized opportunities for sustainable food production. This review discusses the advantages and benefits of utilizing microbes in food production. Microbes can be used as alternative food sources to directly supply nutrients for both humans and livestock. Additionally, microbes offer higher flexibility and diversity in facilitating crop productivity and agri-food production. Microbes function as natural nitrogen fixators, mineral solubilizers, nano-mineral synthesizers, and plant growth regulator inducers, all of which promote plant growth. They are also active organisms in degrading organic materials and remediating heavy metals and pollution in soils, as well as soil-water binders. In addition, microbes that occupy the plant rhizosphere release biochemicals that have nontoxic effects on the host and the environment. These biochemicals could act as biocides in controlling agricultural pests, pathogens, and diseases. Therefore, it is important to consider the use of microbes for sustainable food production.

In vivo surface-enhanced Raman scattering nanosensor for the real-time monitoring of multiple stress signalling molecules in plants

When under stress, plants release molecules to activate their defense system. Detecting these stress-related molecules offers the possibility to address stress conditions and prevent the development of diseases. However, detecting endogenous signalling molecules in living plants remains challenging due to low concentrations of these analytes and interference with other compounds; additionally, many methods currently used are invasive and labour-intensive. Here we show a non-destructive surface-enhanced Raman scattering (SERS)-based nanoprobe for the real-time detection of multiple stress-related endogenous molecules in living plants. The nanoprobe, which is placed in the intercellular space, is optically active in the near-infrared region (785 nm) to avoid interferences from plant autofluorescence. It consists of a Si nanosphere surrounded by a corrugated Ag shell modified by a water-soluble cationic polymer poly(diallyldimethylammonium chloride), which can interact with multiple plant signalling molecules. We measure a SERS enhancement factor of 2.9 × 10⁷ and a signal-to-noise ratio of up to 64 with an acquisition time of ~100 ms. To show quantitative multiplex detection, we adopted a binding model to interpret the SERS intensities of two different analytes bound to the SERS hot spot of the nanoprobe. Under either abiotic or biotic stress, our optical nanosensors can successfully monitor salicylic acid, extracellular adenosine triphosphate, cruciferous phytoalexin and glutathione in Nasturtium officinale, Triticum aestivum L. and Hordeum vulgare L.-all stress-related molecules indicating the possible onset of a plant disease. We believe that plasmonic nanosensor platforms can enable the early diagnosis of stress, contributing to a timely disease management of plants.

Confirmatory detection and identification of biotic and abiotic stresses in wheat using Raman spectroscopy

Wheat is one of the oldest and most widely cultivated staple food crops worldwide. Wheat encounters an array of biotic and abiotic stresses during its growth that significantly impact the crop yield and consequently global food security. Molecular and imaging methods that can be used to detect such stresses are laborious and have numerous limitations. This catalyzes the search for alternative techniques that can be used to monitor plant health. Raman spectroscopy (RS) is a modern analytical technique that is capable of probing structure and composition of samples non-invasively and non-destructively. In this study, we investigate the accuracy of RS in confirmatory diagnostics of biotic and abiotic stresses in wheat. Specifically, we modelled nitrogen deficiency (ND) and drought, key abiotic stresses, and Russian wheat aphid (Diuraphis noxia) infestation and viral diseases: wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), economically significant biotic stresses in common bread wheat. Raman spectra as well as high pressure liquid chromatography (HPLC)-based analyses revealed drastically distinct changes in the intensity of carotenoid vibration (1185 cm^-1) and in the concentration of lutein, chlorophyll, and pheophytin biomolecules of wheat, triggered in response to aforementioned biotic and abiotic stresses. The biochemical changes were reflected in unique vibrational signatures in the corresponding Raman spectra, which, in turn could be used for ~100% accurate identification of biotic and abiotic stresses in wheat. These results demonstrate that a hand-held Raman spectrometer could provide an efficient, scalable, and accurate diagnosis of both biotic as well as abiotic stresses in the field.

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Fertiliser Effect of Ammonia Recovered from Anaerobically Digested Orange Peel Using Gas-Permeable Membranes

The manufacture of mineral N fertilisers by the Haber–Bosch process is highly energy-consuming. The nutrient recovery technologies from wastes through low-cost processes will improve the sustainability of the agricultural systems. This work aimed to assess the suitability of the gas-permeable membrane (GPM) technology to recover N from an anaerobic digestate and test the agronomic behaviour of the ammonium sulphate solution (ASS) obtained. About 62% of the total ammonia nitrogen removed from digestate using GPM was recovered, producing an ASS with 14,889 ± 2324 mg N L−1, which was more than six-fold higher than in digestate. The ASS agronomic behaviour was evaluated by a pot experiment with triticale as a plant test for 34 days in a growth chamber. Compared with the triticale fertilised with the Hoagland solution (Hoag), the ASS provided significantly higher biomass production (+29% dry matter), N uptake (+22%), and higher N agronomic efficiency 3.80 compared with 1.81 mg DM mg−1N in Hoag, and a nitrogen fertiliser replacement value of 133%. These increases can be due to a biostimulant effect provided by the organic compounds of the ASS as assessed by the FT-Raman spectroscopy. The ASS can be considered a bio-based mineral N fertiliser with a biostimulant effect.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

Recent advances in E-monitoring of plant diseases

Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.

Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy

We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm⁻¹ is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

Biochemical Origin of Raman-Based Diagnostics of Huanglongbing in Grapefruit Trees

Biotic and abiotic stresses cause substantial changes in plant biochemistry. These changes are typically revealed by high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS). This information can be used to determine underlying molecular mechanisms of biotic and abiotic stresses in plants. A growing body of evidence suggests that changes in plant biochemistry can be probed by Raman spectroscopy, an emerging analytical technique that is based on inelastic light scattering. Non-invasive and non-destructive detection and identification of these changes allow for the use of Raman spectroscopy for confirmatory diagnostics of plant biotic and abiotic stresses. In this study, we couple HPLC and HPLC-MS findings on biochemical changes caused by Candidatus Liberibacter spp. (Ca. L. asiaticus) in citrus trees to the spectroscopic signatures of plant leaves derived by Raman spectroscopy. Our results show that Ca. L. asiaticus cause an increase in hydroxycinnamates, the precursors of lignins, and flavones, as well as a decrease in the concentration of lutein that are detected by Raman spectroscopy. These findings suggest that Ca. L. asiaticus induce a strong plant defense response that aims to exterminate bacteria present in the plant phloem. This work also suggests that Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level.

Low-power swept-source Raman spectroscopy

'Molecular fingerprinting' with Raman spectroscopy can address important problems-from ensuring our food safety, detecting dangerous substances, to supporting disease diagnosis and management. However, the broad adoption of Raman spectroscopy demands low-cost, portable instruments that are sensitive and use lasers that are safe for human eye and skin. This is currently not possible with existing Raman spectroscopy approaches. Portability has been achieved with dispersive Raman spectrometers, however, fundamental entropic limits to light collection both limits sensitivity and demands high-power lasers and cooled expensive detectors. Here, we demonstrate a swept-source Raman spectrometer that improves light collection efficiency by up to 1000× compared to portable dispersive spectrometers. We demonstrate high detection sensitivity with only 1.5 mW average excitation power and an uncooled amplified silicon photodiode. The low optical power requirement allowed us to utilize miniature chip-scale MEMS-tunable lasers with close to eye-safe optical powers for excitation. We characterize the dynamic range and spectral characteristics of this Raman spectrometer in detail, and use it for fingerprinting of different molecular species consumed everyday including analgesic tablets, nutrients in vegetables, and contaminated alcohol. By moving the complexity of Raman spectroscopy from bulky spectrometers to chip-scale light sources, and by replacing expensive cooled detectors with low-cost uncooled alternatives, this swept-source Raman spectroscopy technique could make molecular fingerprinting more accessible.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

Non-Invasive Identification of Nutrient Components in Grain

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.

Soil Sensors and Plant Wearables for Smart and Precision Agriculture

Soil sensors and plant wearables play a critical role in smart and precision agriculture via monitoring real-time physical and chemical signals in the soil, such as temperature, moisture, pH, and pollutants and providing key information to optimize crop growth circumstances, fight against biotic and abiotic stresses, and enhance crop yields. Herein, the recent advances of the important soil sensors in agricultural applications, including temperature sensors, moisture sensors, organic matter compounds sensors, pH sensors, insect/pest sensors, and soil pollutant sensors are reviewed. Major sensing technologies, designs, performance, and pros and cons of each sensor category are highlighted. Emerging technologies such as plant wearables and wireless sensor networks are also discussed in terms of their applications in precision agriculture. The research directions and challenges of soil sensors and intelligent agriculture are finally presented.

The molecular-physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants

The visual deficiency symptoms developing on plants constitute the ultimate manifestation of suboptimal nutrient supply. In classical plant nutrition, these symptoms have been extensively used as a tool to characterise the nutritional status of plants and to optimise fertilisation. Here we expand this concept by bridging the typical deficiency symptoms for each of the six essential macronutrients to their molecular and physiological functionalities in higher plants. We focus on the most recent insights obtained during the last decade, which now allow us to better understand the links between symptom and function for each element. A deep understanding of the mechanisms underlying the visual deficiency symptoms enables us to thoroughly understand how plants react to nutrient limitations and how these disturbances may affect the productivity and biodiversity of terrestrial ecosystems. A proper interpretation of visual deficiency symptoms will support the potential for sustainable crop intensification through the development of new technologies that facilitate automatised management practices based on imaging technologies, remote sensing and in-field sensors, thereby providing the basis for timely application of nutrients via smart and more efficient fertilisation.

Species-independent analytical tools for next-generation agriculture

Innovative approaches are urgently required to alleviate the growing pressure on agriculture to meet the rising demand for food. A key challenge for plant biology is to bridge the notable knowledge gap between our detailed understanding of model plants grown under laboratory conditions and the agriculturally important crops cultivated in fields or production facilities. This Perspective highlights the recent development of new analytical tools that are rapid and non-destructive and provide tissue-, cell- or organelle-specific information on living plants in real time, with the potential to extend across multiple species in field applications. We evaluate the utility of engineered plant nanosensors and portable Raman spectroscopy to detect biotic and abiotic stresses, monitor plant hormonal signalling as well as characterize the soil, phytobiome and crop health in a non- or minimally invasive manner. We propose leveraging these tools to bridge the aforementioned fundamental gap with new synthesis and integration of expertise from plant biology, engineering and data science. Lastly, we assess the economic potential and discuss implementation strategies that will ensure the acceptance and successful integration of these modern tools in future farming practices in traditional as well as urban agriculture.

Portable Raman leaf-clip sensor for rapid detection of plant stress

Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi (Brassica rapa chinensis) and Choy Sum (Brassica rapa var. parachinensis). In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.

Rapid metabolite response in leaf blade and petiole as a marker for shade avoidance syndrome

BACKGROUND

Shade avoidance syndrome (SAS) commonly occurs in plants experiencing vegetative shade, causing morphological and physiological changes that are detrimental to plant health and consequently crop yield. As the effects of SAS on plants are irreversible, early detection of SAS in plants is critical for sustainable agriculture. However, conventional methods to assess SAS are restricted to observing for morphological changes and checking the expression of shade-induced genes after homogenization of plant tissues, which makes it difficult to detect SAS early.

RESULTS

Using the model plant Arabidopsis thaliana, we introduced the use of Raman spectroscopy to measure shade-induced changes of metabolites in vivo. Raman spectroscopy detected a decrease in carotenoid contents in leaf blades and petioles of plants with SAS, which were induced by low Red:Far-red light ratio or high density conditions. Moreover, by measuring the carotenoid Raman peaks, we were able to show that the reduction in carotenoid content under shade was mediated by phytochrome signaling. Carotenoid Raman peaks showed more remarkable response to SAS in petioles than leaf blades of plants, which greatly corresponded to their morphological response under shade or high plant density. Most importantly, carotenoid content decreased shortly after shade induction but before the occurrence of visible morphological changes. We demonstrated this finding to be similar in other plant species. Comprehensive testing of Brassica vegetables showed that carotenoid content decreased during SAS, in both shade and high density conditions. Likewise, carotenoid content responded quickly to shade, in a manner similar to Arabidopsis plants.

CONCLUSIONS

In various plant species tested in this study, quantification of carotenoid Raman peaks correlate to the severity of SAS. Moreover, short-term exposure to shade can induce the carotenoid Raman peaks to decrease. These findings highlight the carotenoid Raman peaks as a biomarker for early diagnosis of SAS in plants.

A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery

Assessment of the nitrogen status of grapevines with high spatial, temporal resolution offers benefits in fertilizer use efficiency, crop yield and quality, and vineyard uniformity. The primary objective of this study was to develop a robust predictive model for grapevine nitrogen estimation at bloom stage using high-resolution multispectral images captured by an unmanned aerial vehicle (UAV). Aerial imagery and leaf tissue sampling were conducted from 150 grapevines subjected to five rates of nitrogen applications. Subsequent to appropriate pre-processing steps, pixels representing the canopy were segmented from the background per each vine. First, we defined a binary classification problem using pixels of three vines with the minimum (low-N class) and two vines with the maximum (high-N class) nitrogen concentration. Following optimized hyperparameters configuration, we trained five machine learning classifiers, including support vector machine (SVM), random forest, XGBoost, quadratic discriminant analysis (QDA), and deep neural network (DNN) with fully-connected layers. Among the classifiers, SVM offered the highest F1-score (82.24%) on the test dataset at the cost of a very long training time compared to the other classifiers. Alternatively, QDA and XGBoost required the minimum training time with promising F1-score of 80.85% and 80.27%, respectively. Second, we transformed the classification into a regression problem by averaging the posterior probability of high-N class for all pixels within each of 150 vines. XGBoost exhibited a slightly larger coefficient of determination (R2 = 0.56) and lower root mean square error (RMSE) (0.23%) compared to other learning methods in the prediction of nitrogen concentration of all vines. The proposed approach provides values in (i) leveraging high-resolution imagery, (ii) investigating spatial distribution of nitrogen across a vine’s canopy, and (iii) defining spatial zones for nitrogen application and smart sampling.