Recent developments in fast spectroscopy for plant mineral analysis

Review - Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor

Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.

Macronutrient omission influences morphological parameters, growth, and yield in Arracacia xanthorrhiza Bancroft

Mineral nutrition in arracacha is a critical production factor that conditions harvest yield. Few studies have been developed in nutrition and physiology, this does not allow to the design of ideal fertilization programs; consequences are increased production costs, soil degradation, and low-quality storage roots. Therefore, this study aimed to characterize the symptoms associated with macronutrient deficiency in arracacha plants and its effect on morphological parameters, the accumulation of fresh and dry biomass, and the distribution of dry matter in the different organs. Under greenhouse conditions, the experiment was conducted in Cajamarca, Tolima, Colombia. A completely randomized design was implemented, with seven treatments and six replicates (6 solutions lacking N, P, K, Ca, Mg, and S and Hoagland complete solution). Forty-two seedlings were transplanted, to which the complete solution was applied for 75 days, increasing the concentrations from 0.25 M to 1 M, and then nutritional deficiencies were induced. Deficiencies caused by macronutrients in arracacha plants exhibited visual symptoms and changes in their morphology. The omission of N, Ca, and S generated the most severe symptoms, drastically affecting plant height, leaf width, number of leaves, and plant mass accumulation. In the case of P, leaves became small and intense green with a violet margin. The Mg and K generated leaves with interveinal and margin chlorosis. Plants with the omission of macronutrients allocated dry mass in the following order: stem, storage roots, propagules, and leaves.

Roadmapping 5.0 Technologies in Agriculture: A Technological Proposal for Developing the Coffee Plant Centered on Indigenous Producers' Requirements from Mexico, via Knowledge Management

The coffee plant, with more than 40 billion shrubs, 9 million tons of grains produced, and 80% of its production accounted for by small-scale producers, has been severely damaged since the emergence of Hemileia vastatrix and Hypothenemus hampei. Despite technological support, these pests have caused 20% to 40% production losses, a 50% to 60% deficit in performance, and a cost of between USD 70 million and USD 220 million to the world economies, which forces us to rethink actions centered on people as the key elements to develop appropriate solutions. For this, the present study presents a technological proposal centered on small indigenous coffee producer requirements for introducing Industry 5.0 technologies, considering roadmapping, knowledge management, statistical analysis, and the social, productive, and digital contexts of five localities in Mexico. The results show a correlation between monitoring and control, soil analysis, the creation of organic fertilizers, accompaniment, and coffee experimentation, as the actions to be implemented, proposing the introduction of a mobile application; sensors, virtual platforms, dome-shaped greenhouses, and spectrophotometric technology as relevant technologies centered on indigenous coffee producers' requirements. This study is important for policymakers, academics, and producers who wish to develop strategies centered on people in Mexico and the world.

Rapid monitoring of flavonoid content in sweet tea (Lithocarpus litseifolius (Hance) Chun) leaves using NIR spectroscopy

BACKGROUND

Sweet tea, which functions as tea, sugar and medicine, was listed as a new food resource in 2017. Flavonoids are the main medicinal components in sweet tea and have significant pharmacological activities. Therefore, the quality of sweet tea is related to the content of flavonoids. Flavonoid content in plants is normally determined by time-consuming and expensive chemical analyses. The aim of this study was to develop a methodology to measure three constituents of flavonoids, namely, total flavonoids, phloridin and trilobatin, in sweet tea leaves using near-infrared spectroscopy (NIR).

RESULTS

In this study, we demonstrated that the combination of principal component analysis (PCA) and NIR spectroscopy can distinguish sweet tea from different locations. In addition, different spectral preprocessing methods are used to establish partial least squares (PLS) models between spectral information and the content of the three constituents. The best total flavonoid prediction model was obtained with NIR spectra preprocessed with Savitzky-Golay combined with second derivatives (SG + D2) (R_P² = 0.893, and RMSEP = 0.131). For trilobatin, the model with the best performance was developed with raw NIR spectra (R_P² = 0.902, and RMSEP = 2.993), and for phloridin, the best model was obtained with NIR spectra preprocessed with standard normal variate (SNV) (R_P² = 0.818, and RMSEP = 1.085). The coefficients of determination for all calibration sets, validation sets and prediction sets of the best PLS models were higher than 0.967, 0.858 and 0.818, respectively.

CONCLUSIONS

The conclusion indicated that NIR spectroscopy has the ability to determine the flavonoid content of sweet tea quickly and conveniently.

Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy

Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R2 = 0.96, root mean relative error (RMSE) = 1.03 g kg-1) and the test datasets (R2 = 0.91, RMSE = 1.29 g kg-1), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.

Iron Deficiency Leads to Chlorosis Through Impacting Chlorophyll Synthesis and Nitrogen Metabolism in Areca catechu L

Deficiency of certain elements can cause leaf chlorosis in Areca catechu L. trees, which causes considerable production loss. The linkage between nutrient deficiency and chlorosis phenomenon and physiological defect in A. catechu remains unclear. Here, we found that low iron supply is a determinant for chlorosis of A. catechu seedling, and excessive iron supply resulted in dark green leaves. We also observed morphological characters of A. catechu seedlings under different iron levels and compared their fresh weight, chlorophyll contents, chloroplast structures and photosynthetic activities. Results showed that iron deficiency directly caused chloroplast degeneration and reduced chlorophyll synthesis in chlorosis leaves, while excessive iron treatment can increase chlorophyll contents, chloroplasts sizes, and inflated starch granules. However, both excessive and deficient of iron decreases fresh weight and photosynthetic rate in A. catechu seedlings. Therefore, we applied transcriptomic and metabolomic approaches to understand the effect of different iron supply to A. catechu seedlings. The genes involved in nitrogen assimilation pathway, such as NR (nitrate reductase) and GOGAT (glutamate synthase), were significantly down-regulated under both iron deficiency and excessive iron. Moreover, the accumulation of organic acids and flavonoids indicated a potential way for A. catechu to endure iron deficiency. On the other hand, the up-regulation of POD-related genes was assumed to be a defense strategy against the excessive iron toxicity. Our data demonstrated that A. catechu is an iron-sensitive species, therefore the precise control of iron level is believed to be the key point for A. catechu cultivation.

Photosynthetic Parameters Show Specific Responses to Essential Mineral Deficiencies

In response to decreases in the assimilation efficiency of CO2, plants oxidize the reaction center chlorophyll (P700) of photosystem I (PSI) to suppress reactive oxygen species (ROS) production. In hydro-cultured sunflower leaves experiencing essential mineral deficiencies, we analyzed the following parameters that characterize PSI and PSII: (1) the reduction-oxidation states of P700 [Y(I), Y(NA), and Y(ND)]; (2) the relative electron flux in PSII [Y(II)]; (3) the reduction state of the primary electron acceptor in PSII, QA (1 - qL); and (4) the non-photochemical quenching of chlorophyll fluorescence (NPQ). Deficiency treatments for the minerals N, P, Mn, Mg, S, and Zn decreased Y(II) with an increase in the oxidized P700 [Y(ND)], while deficiencies for the minerals K, Fe, Ca, B, and Mo decreased Y(II) without an increase in Y(ND). During the induction of photosynthesis, the above parameters showed specific responses to each mineral. That is, we could diagnose the mineral deficiency and identify which mineral affected the photosynthesis parameters.

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R² ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R² ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

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.

Yield response to soil test phosphorus in Switzerland: Pedoclimatic drivers of critical concentrations for optimal crop yields using multilevel modelling

Phosphorus (P) management in agroecosystems is driven by opposing requirements in agronomy, ecology, and environmental protection. The widely used maintenance P fertilization strategy relies on critical concentrations of soil test P (STP), which should cause the lowest possible impact on the environment while still ensuring optimal yield. While both soil P availability and crop yields are fundamentally related to pedoclimatic conditions, little is known about the extent to which soil and climate variables control critical STP. The official P fertilization guidelines for arable crops in Switzerland are based on empirically derived critical concentrations for two soil test methods (H₂O-CO₂ and AAE10). To validate those values and evaluate their relation to pedoclimatic conditions, we established nonlinear multivariate multilevel yield response models fitted to long-term data from six sites. The Mitscherlich function proved most suitable out of three functions and model fit was significantly enhanced by taking the multilevel data structure into account. Yield response to STP was strongest for potato, intermediate for barley, and lowest for wheat and maize. Mean critical STP at 95% maximum yield ranged among crops from 0.15-0.58 mg kg^-1 (H₂O-CO₂) and 0-36 mg kg^-1 (AAE10). However, pedoclimatic conditions such as annual temperature or soil clay content had a large impact on critical STP, entailing changes of up to 0.9 mg kg^-1 (H₂O-CO₂) and 80 mg kg^-1 (AAE10). Critical STP for the AAE10 method was also affected by soil pH. Our findings suggest that the current Swiss fertilization guidelines overestimate actual crop P demand on average and that site conditions account for large parts of the variation in critical STP. We propose that site-specific fertilization recommendations could be improved on the basis of agro-climate classes in addition to soil information, which can help to counteract the accumulation of unutilized soil P by long-term P application.

Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems

The presence of cadmium in rice stems is a limiting factor that restricts its function as biomass. In order to prevent potential risks of heavy metals in rice straws, this study introduced a fast detection method of cadmium in rice stems based on laser induced breakdown spectroscopy (LIBS) and chemometrics. The wavelet transform (WT), area normalization and median absolute deviation (MAD) were used to preprocess raw spectra to improve spectral stability. Principal component analysis (PCA) was used for cluster analysis. The classification models were established to distinguish cadmium stress degree of stems, of which extreme learning machine (ELM) had the best effect, with 91.11% of calibration accuracy and 93.33% of prediction accuracy. In addition, multivariate models were established for quantitative detection of cadmium. It can be found that ELM model had the best prediction effects with prediction correlation coefficient of 0.995. The results show that LIBS provides an effective method for detection of cadmium in rice stems. The combination of LIBS technology and chemometrics can quickly detect the presence of cadmium in rice stems, and accurately realize qualitative and quantitative analysis of cadmium, which could be of great significance to promote the development of new energy industry.

Nutrient Diagnosis of Fertigated "Prata" and "Cavendish" Banana (Musa spp.) at Plot-Scale

Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 "Prata" and "Cavendish" plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models.

Assessing the variation in manganese use efficiency traits in Scottish barley landrace Bere (Hordeum vulgare L.)

BACKGROUND AND AIMS

Manganese (Mn) deficiency in barley is a global problem. It is difficult to detect in the early stages of symptom development and is commonly pre-emptively corrected by Mn foliar sprays that can be costly. Landraces adapted to marginal lands around the world represent a genetic resource for potential sustainability traits including mineral use efficiency. This research aims to confirm novel Mn use efficiency traits from the Scottish landrace Bere and use an association mapping approach to identify genetic loci associated with the trait.

METHODS

A hydroponic system was developed to identify and characterize the Mn deficiency tolerance traits in a collection of landraces, including a large number of Scottish Bere barleys, a group of six-rowed heritage landraces grown in the highlands and islands of Scotland. Measuring chlorophyll fluorescence, the effect of Mn deficiency was identified in the early stages of development. Genotypic data, generated using the 50k Illumina iSelect genotyping array, were coupled with the Mn phenotypic data to create a genome-wide association study (GWAS) identifying candidate loci associated with Mn use efficiency.

KEY RESULTS

The Bere lines generally had good Mn use efficiency traits. Individual Bere lines showed large efficiencies, with some Bere lines recording almost double chlorophyll fluorescence readings in limited Mn conditions compared with the elite cultivar Scholar. The Mn-efficient Bere lines had increased accumulation of Mn in their shoot biomass compared with elite cultivars, which was highly correlated to the chlorophyll fluorescence. Several candidate genes were identified as being associated with Mn use efficiency in the GWAS.

CONCLUSIONS

Several genomic regions for Mn use efficiency traits originating from the Bere lines were identified. Further examination and validation of these regions should be undertaken to identify candidate genes for future breeding for marginal lands.

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage

The recent development of portable X-ray fluorescence spectrometers (PXRF) has created new avenues for rapid plant elemental concentration determination at reduced cost while avoiding hazardous chemicals. A few studies have indicated the potential use of PXRF for homogenous plant tissue analysis. However, there is a lack of information for analysis of heterogeneous plant samples like livestock forage, which consists of a mixture of several species and plant parts, each varying in elemental concentration. Our objective was to evaluate PXRF for forage analysis, specifically the effect of forage particle size and scan time on important elements including P, K, Ca, and Fe determination. Hay samples (n = 42) were oven dried (60°C for 3 days) and ground into three particle sizes (≤0.5 mm, 0.25-0.5 mm and 1-2 mm). Prepared samples were scanned by PXRF using a vacuum (<10 torr) without a filter. Samples were placed in cups over thin prolene X-ray film and scanned for 180 s. A subset (n = 29) were also scanned for 60 and 120 s. PXRF counts for P, K, Ca, and Fe were compared with laboratory Inductively Coupled Plasma Optical Emission Spectroscopy (ICP) determinations, using regression models. Results indicated that these elements could potentially be determined with PXRF (r ² ≥ 0.70) in heterogeneous forage samples. Relationship strength increased with decreasing particle size, however, the relationship was still strong (r ² ≥ 0.57) at the largest particle size. Scanning time did not affect the relationship with ICP concentration for any of the particle sizes evaluated. This work demonstrated that with the right sample preparation PXRF can obtain results comparable to acid digestion and ICP regardless of sample composition, and suggests the potential for in situ determinations.

Breeding to adapt agriculture to climate change: affordable phenotyping solutions

Breeding is one of the central pillars of adaptation of crops to climate change. However, phenotyping is a key bottleneck that is limiting breeding efficiency. The awareness of phenotyping as a breeding limitation is not only sustained by the lack of adequate approaches, but also by the perception that phenotyping is an expensive activity. Phenotyping is not just dependent on the choice of appropriate traits and tools (e.g. sensors) but relies on how these tools are deployed on their carrying platforms, the speed and volume of data extraction and analysis (throughput), the handling of spatial variability and characterization of environmental conditions, and finally how all the information is integrated and processed. Affordable high throughput phenotyping aims to achieve reasonably priced solutions for all the components comprising the phenotyping pipeline. This mini-review will cover current and imminent solutions for all these components, from the increasing use of conventional digital RGB cameras, within the category of sensors, to open-access cloud-structured data processing and the use of smartphones. Emphasis will be placed on field phenotyping, which is really the main application for day-to-day phenotyping.

Manganese Deficiency in Plants: The Impact on Photosystem II

Manganese (Mn) is an essential plant micronutrient with an indispensable function as a catalyst in the oxygen-evolving complex (OEC) of photosystem II (PSII). Even so, Mn deficiency frequently occurs without visual leaf symptoms, thereby masking the distribution and dimension of the problem restricting crop productivity in many places of the world. Hence, timely alleviation of latent Mn deficiency is a challenge in promoting plant growth and quality. We describe here the key mechanisms of Mn deficiency in plants by focusing on the impact of Mn on PSII stability and functionality. We also address the mechanisms underlying the differential tolerance towards Mn deficiency observed among plant genotypes, which enable Mn-efficient plants to grow on marginal land with poor Mn availability.