Leaf Soil Plant Analysis Development Research Articles

Identification of quantitative trait loci (QTLs) regulating leaf SPAD value and trichome density in mungbean (Vigna radiata L.) using genotyping-by-sequencing (GBS) approach.

Quantitative trait loci (QTL) mapping is used for the precise localization of genomic regions regulating various traits in plants. Two major QTLs regulating Soil Plant Analysis Development (SPAD) value (qSPAD-7-1) and trichome density (qTric-7-2) in mungbean were identified using recombinant inbred line (RIL) populations (PMR-1×Pusa Baisakhi) on chromosome 7. Functional analysis of QTL region identified 35 candidate genes for SPAD value (16 No) and trichome (19 No) traits. The candidate genes regulating trichome density on the dorsal leaf surface of the mungbean include VRADI07G24840, VRADI07G17780, and VRADI07G15650, which encodes for ZFP6, TFs bHLH DNA-binding superfamily protein, and MYB102, respectively. Also, candidate genes having vital roles in chlorophyll biosynthesis are VRADIO7G29860, VRADIO7G29450, and VRADIO7G28520, which encodes for s-adenosyl-L-methionine, FTSHI1 protein, and CRS2-associated factor, respectively. The findings unfolded the opportunity for the development of customized genotypes having high SPAD value and high trichome density having a possible role in yield and mungbean yellow vein mosaic India virus (MYMIV) resistance in mungbean.

Stratified diagnosis of cotton canopy spectral characteristics based on CWT-SPA and its relationship with moisture, nitrogen, and SPAD values

ABSTRACT Cotton is an economically valuable crop worldwide, and accurate and rapid leaf moisture content, nitrogen content, and soil plant analysis development (SPAD) value estimations are crucial for cotton growth. In this study, high-resolution spectral data were collected for the Tahe 2 cotton variety in Alar City, Xinjiang. The raw spectra were preprocessed using four methods: first derivative, standard normal variate transformation, second derivative, and multiplicative scatter correction. Wavelet coefficients at multiple scales were generated from the raw spectra using a continuous wavelet transform (CWT) and CWT with first derivative (CWT-FD). A quantitative detection model combining CWT and CWT-FD with successive projection algorithm – partial least squares (SPA-PLS) was developed to estimate the leaf moisture content, nitrogen content, and SPAD values. The performances of the different scales and methods for estimating these parameters were compared. Six stratification methods were employed to determine the optimal modelling results and decomposition scales for the parameters under various stratification schemes. The results demonstrated that the CWT and CWT-FD methods significantly improved the accuracy of estimating cotton canopy spectral characteristics related to the parameters, surpassing that of conventional spectral processing methods. The mexh, db5, and db5 wavelet basis functions, along with scale 3, were identified as the optimal modelling choices for moisture content, nitrogen content, and SPAD values throughout the cotton plant growth period. Among the six vertical stratification structures of cotton, the CWT and CWT-FD methods, along with the three wavelet basis functions, achieved the best results for the three parameters in the upper layer. The optimal relative percent deviation values for the three parameters of typical leaves ranged from 1.603 to 2.016, indicating satisfactory modelling performance. These findings provide a theoretical foundation for accurately estimating leaf moisture content, nitrogen content, and SPAD values using spectral technology, enabling the simultaneous extraction and monitoring of multiple physiological indicators.

Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates.

Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM

Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture.

High-tunnel Organic Ginger: Response to Propagation Material, Fertilizer, and Prepropagation Rhizome Storage

Ginger (Zingiber officinale, Roscoe) is a tropical rhizome crop typically grown from rhizome pieces, but can also be produced from seedlings. No information is available on how the seedling method compares with the rhizome piece method in organic ginger culture. In addition, information on the growing of organic ginger in the mid-Atlantic region is lacking. Some of the challenges include limited knowledge of rhizome storage, types of propagation materials for planting in the field or high tunnel, and acceptable organic fertilizers that will not increase the excess P currently polluting the Chesapeake Bay watershed. The objective of this study was to assess plant development, soil nutrients, and economic feasibility of organic ginger derived from different storage conditions and planting materials when grown in different nutrient sources in a high tunnel. Three types of plant material (single-shoot transplant seedlings derived from 36.5–40.0 g/rhizome, multishoot transplant seedlings derived from 60–120 g/rhizome, and rhizome seeds of 60–120 g) and three fertilizers types [cotton seed meal, 6N–0.9P–0.8K (0.18 kg⋅m–2), plus AZOMITE (1 kg⋅m–2); Nature Safe, 13N–0P–0K (0.07 kg⋅m–2); and Phytamin All Purpose Liquid fertilizer, 4N–1.3P–3.3K (0.26 L⋅m–2)] were used in 2018. In 2019 and 2020, three types of plant material and two fertilizer types at modified rates from the 2018 study, plus two storage containers (pans and flats), were tested. In general, the rhizome storage container did not affect plant height, leaf soil plant analysis development (SPAD) index, and rhizome yield, and its effect on tillers was none or mixed. Fertilizer type had mixed effects on plant height and tiller number, and no effect on the leaf SPAD index. Rhizome yields in 2019 and 2020 were unaffected by fertilizer, but Nature Safe produced a greater benefit-to-cost ratio (BCR) and profitability index (PI) than Phytamin. Soil P was generally less in Nature Safe–fertilized soil than in Phytamin-fertilized soil. Multishoot seedlings produced the greatest rhizome yield, BCR, PI, and tallest plants, and had some of the highest tiller numbers. These findings show that it would be more profitable to use multishoot seedlings as planting material in high tunnels compared with single-shoot seedlings and rhizome seeds. Furthermore, the lower P levels in the Nature Safe–fertilized soils compared with the Phytamin soils, and greater PI suggest that using Nature Safe will be a better choice than Phytamin for growing organic ginger.

Effects of optimized nitrogen fertilizer management on the yield, nitrogen uptake, and ammonia volatilization of direct-seeded rice.

Direct-seeded rice has been developed rapidly because of labor savings. Changes in rice cultivation methods put forward new requirements for nitrogen (N) fertilizer management practices. Field experiments with five different fertilizer ratios of basal, tillering and panicle fertilizer, namely N1 (10:0:0), N2 (6:2:2), N3 (4:3:3), N4 (2:4:4) and N5 (0:5:5), were conducted to investigate the effects of different N fertilizer management practices on yield formation, N uptakes, and ammonia (NH3 ) volatilization from paddy fields in direct-seeded rice. The results showed that the N4 treatment improved grain yield by 5.1% while decreasing NH3 volatilization by 20.4% compared with that of conventional fertilizer treatment (N2). The panicle number per unit area was the key factor to determine the yield of direct-seeded rice (72%). Excessive N application of basal fertilizer (N1) reduced seedling emergence, N use efficiency, and yield by 45.3%, 160.6%, and 6.9% respectively and increased NH3 volatilization by 28.1% compared with that of the N4 treatment. Removal of basal N fertilizer (N5) N reduced spike number and yield by 13.0% and 6.9% respectively, minimizing NH3 volatilization while affecting the construction of high-yielding populations compared with that of the N4 treatment. Optimized N fertilizer management achieved delayed senescence (maintenance of higher leaf Soil Plant Analysis Development meter values in late reproduction), higher canopy photoassimilation (suitable leaf area), higher N fertilizer use efficiency, and less N loss (lower cumulative NH3 volatilization). © 2023 Society of Chemical Industry.

Responses of Phragmites australis to Nitrogen Addition along Salinity Gradients in Coastal Saline–Alkali Soil

Soil salinization and nitrogen (N) enrichment in saline–alkali soils resulting from human activities cause potential environmental pressure on Phragmites australis. However, the response of P. australis to N addition under different salt conditions remains unknown. This study examined the changes in soil properties and growth indices as well as their relationship to N addition through an in situ field experiment using three soil salinity levels with P. australis in the Yellow River Delta. The study showed that soil salinity levels significantly affected the effects of N addition on soil pH and water contents. N addition increased the soil NO3– contents and decreased soil available phosphorus (Avail. P) contents; however, soil salinity levels did not impact the effects of N addition on soil NO3− and Avail. P contents. N addition decreased the biomass of P. australis, since the decrease in the competitiveness for N sources changed the vegetation diversity. The results suggest that the biomass, plant height, and leaf soil plant analysis development (SPAD) values of P. australis increased with increasing soil Avail. P contents rather than soil NO3– contents. Therefore, we suggest the important role of Avail. P addition in N enrichment conditions in saline–alkali wasteland and estuarine wetland ecosystems.

Reducing and Delaying Nitrogen Recommended by Leaf Critical SPAD Value Was More Suitable for Nitrogen Utilization of Spring Wheat under a New Type of Drip-Irrigated System

Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha−1), N2 (270 kg ha−1), N3 (240 kg ha−1) and N4 (0 kg ha−1)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r2) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha−1, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.

Post-flowering Soil Waterlogging Curtails Grain Yield Formation by Restricting Assimilates Supplies to Developing Grains.

Soil waterlogging is among the major factors limiting the grain yield of winter wheat crops in many parts of the world, including the middle and lower reaches of the Yangtze River China. In a field study, we investigated the relationship between leaf physiology and grain development under a varying duration of post-flowering waterlogging. A winter wheat cultivar Ningmai 13 was exposed to soil waterlogging for 0 (W0), 3 (W3), 6 (W6), and 9 d (W9) at anthesis. Increasing waterlogging duration significantly reduced flag leaf SPAD (soil plant analysis development) values and net photosynthetic rate (Pn). There was a linear reduction in flag leaf Pn and SPAD as plant growth progressed under all treatments; however, the speed of damage was greater in the waterlogged leaves. For example, compared with their respective control (W0), flag leaves of W9 treatment have experienced 46% more reduction in Pn at 21 d after anthesis (DAA) than at 7 DAA. Increasing waterlogging duration also induced oxidative damage in flag leaves, measured as malondialdehyde (MDA) contents. The capacity to overcome this oxidative damage was limited by the poor performance of antioxidant enzymes in wheat leaves. Inhibited leaf Pn and capacity to sustain assimilate synthesis under waterlogged environments reduced grain development. Compared with W0, W6 and W9 plants experienced a 20 and 22% reduction in thousand grain weight (TGW) in response to W6 and W9, respectively at 7 DAA and 11 and 19%, respectively at 28 DAA. Sustained waterlogging also significantly reduced grain number per spike and final grain yield. Averaged across two years of study, W9 plants produced 28% lesser final grain yield than W0 plants. Our study suggested that wheat crops are highly sensitive to soil waterlogging during reproductive and grain filling phases due to their poor capacity to recover from oxidative injury to photosynthesis. Management strategies such as planting time, fertilization and genotype selection should be considered for the areas experiencing frequent waterlogging problems.

Effects of nitrogen application rates on the spatio-temporal variation of leaf SPAD readings on the maize canopy

AbstractThe spatio-temporal variation of leaf chlorophyll content is an important crop phenotypic trait that is of great significance for evaluating crop productivity. This study used a soil-plant analysis development (SPAD) chlorophyll meter for non-destructive monitoring of leaf chlorophyll dynamics to characterize the patterns of spatio-temporal variation in the nutritional status of maize (Zea maysL.) leaves under three nitrogen treatments in two cultivars. The results showed that nitrogen levels could affect the maximum leaf SPAD reading (SPADmax) and the duration of high SPAD reading. A rational model was used to measure the changes in SPAD readings over time in single leaves. This model was suitable for predicting the dynamics of the nutrient status for each leaf position under different nitrogen treatments, and model parameter values were position dependent.SPADmaxat each leaf decreased with the reduction of nitrogen supply. Leaves at different positions in both cultivars responded differently to higher nitrogen rates. Lower leaves (8th–10th positions) were more sensitive than the other leaves in response to nitrogen. Monitoring the SPAD reading dynamic of lower leaves could accurately characterize and assess the nitrogen supply in plants. The lower leaves in nitrogen-deficient plants had a shorter duration of high SPAD readings compared to nitrogen-sufficient plants; this physiological mechanism should be studied further. In summary, the spatio-temporal variation of plant nitrogen status in maize was analysed to determine critical leaf positions for potentially assisting in the identification of appropriate agronomic management practices, such as the adjustment of nitrogen rates in late fertilization.

Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its , , and are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

Development of a low-cost portable device for pixel-wise leaf SPAD estimation and blade-level SPAD distribution visualization using color sensing

Soil Plant Analysis Development (SPAD) was proved as a reliable indicator for the representation of leaf chlorophyll or nitrogen content. Portable plant SPAD sensors have been widely studied to determine plant nitrogen status rapidly in recent years. However, the SPAD-502 device could only measure a single point in one shot, and its price was high. Moreover, most of the presented DIY devices for SPAD evaluation required clamping operation to get the transmitted light signal using complex hardware architecture. In this study, an ultra-portable SPAD evaluation system, defined as SPAD-Cap, was proposed for leaf SPAD distribution analysis. The RGB camera module, Raspberry Pi Zero, and light source were integrated. Two modeling methods, including partial least square regression (PLSR) and convolutional neural network for regression (CNN-R), were used to evaluate pixel-level SPAD value based on the color features of the corresponding pixel. The SPAD distribution map was generated by traversing all pixels of the tested blade sample using the trained model. A Web GUI was developed for controlling the whole system and accessing the visualization result. The testing result showed that the designed SPAD-Cap achieved R2 of 0.97 and RMSE of 2.5 SPAD units to inspect rape leaf SPAD. For leaves in several species, R2 was 0.91, and RMSE was about 4.0 SPAD. Moreover, all the processing steps, including data collection, processing, storage, and visualization, were involved in the SPAD-Cap system, which could be a potential solution for rapid and automatic inspection of leaf SPAD distribution.

Recommended nitrogen rates and the verification of effects based on leaf SPAD readings of rice.

Modern rice production systems need a reliable, easy-to-use, efficient, and environmentally-friendly method to determine plant nitrogen (N) status , predict grain yield, and optimize N management. We conducted field experiments to determine the influence of different N rates on Soil Plant Analysis Development (SPAD) readings of rice leaves. We also performed field validations to evaluate the grain yield and N use efficiency under recommended N rates. Our results showed that leaf SPAD readings increased as N rates increased. We applied the recommended N based on the relationships between the N rates and leaf SPAD readings at the tillering and booting stages. The recommended N decreased N rates and improved N use efficiency without sacrificing grain yield. When compared to farmer practices (FP), the recommended N rates of optimization (OPT) decreased by 5.8% and 10.0%, respectively. In comparison with FP, the N agronomic efficiency of OPT increased by 5.8 and 10.0% while the partial factor productivity of N increased by 6.0 and 14.2%, respectively. The SPAD meter may be a reliable tool to analyze the N in rice, estimate real-time N fertilization, and improve N use efficiency.

Genetic parameters and selection indexes for biofortified red leaf lettuce

Abstract: The objective of this work was to determine the genetic parameters and the efficiency of different selection indices for biofortified red leaf lettuce (Lactuca sativa var. crispa) lines with agronomic and nutritional potential. The experiment was carried out in a randomized complete block design with three replicates and 31 crisp-textured and red-tinted leaf lettuce genotypes: the Belíssima cultivar, with a low carotenoid content and rich in anthocyanins; and 30 lines from the cross between the Belíssima and Uberlândia 10000 cultivars, rich in carotenoids and with a low anthocyanin content. The assessed traits were: total green mass (g), stem diameter (cm), leaf count, plant diameter (cm), foliar temperature (°C), soil plant analysis development (SPAD) index, anthocyanin content (mg 100 g-1 sample), and bolting (days after sowing). To estimate selection gains, 12 genotypes were selected through selection indices. The traditional index proposed by Smith & Hazel and the sum of ranks index by Mulamba & Mock provided the highest selection gains in the biofortified lettuce. The coefficient of genotypic determination for leaf count, anthocyanin content, bolting, and SPAD index is of high magnitude.

Influence of simulated acid rain on the physiological response of flowering Chinese cabbage and variation of soil nutrients

Flowering Chinese cabbages are widely planted in the south of China and often exposed to acid rain. However, the effect of acid rain on the growth of flowering Chinese cabbage is unclear. In this study, we investigated the influence of simulated acid rain (SAR) on plant height, soil-plant analysis development (SPAD) value (an index for chlorophyll content), proline, malondialdehyde (MDA), antioxidant enzyme activities, nitrogen (N), phosphorus (P), or potassium (K) uptake and variation of soil nutrients. Our results showed that SAR at pH 5.5 did not damage plant development because growth characteristics, photosynthesis, and superoxide dismutase and peroxidase activities did not change obviously at this pH compared to those at pH 7.0. However, 2- to 7-time of SAR exposure at pH 4.5 and pH 3.5 led to the increases of antioxidant enzyme activities, MDA and proline contents, and the decreases of leaf SPAD value and root activity. Nutrient analysis indicated that spraying 4 to 7 times of SAR at pH 3.5 reduced the uptake of N, P and K of flowering Chinese cabbage significantly. In addition, treatment with SAR at pH 3.5 decreased the pH value of the surface soil and the contents of alkaline-hydrolytic N and readily available K but increased that of readily available P in the surface soil by 8.5% to 14.9%. Taken together, our results indicated that SAR at pH 3.5 influenced the antioxidant enzyme system and the contents of soil nutrients, caused metabolic disorders and ultimately restricted the development and growth of flowering Chinese cabbages.

Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning

Soybean (Glycine max (L.) Merrill) leaf chlorophyll content is indicative of the plant growth and health issues. However, chlorophyll measurement using the standard chemical procedure is laborious, while the sensor-based electronic options, such as soil plant analysis development (SPAD) meter tend to be highly expensive and made only spot measurements. Therefore, a simpler and less expensive infield method of chlorophyll measurement in soybeans using smartphone camera with image processing and machine learning models was developed. Soybean leaf images (720 images) and SPAD readings were collected from different cultivars (4), with replications (3) and sampling dates (2) from experimental plots. Of the several color vegetation indices (CVIs) tested, the dark green color index (DGCI) had the best correlation with SPAD meter readings (r=0.90), which was further improved by color calibration (r=0.93). The results of the random coefficients model showed that both cultivars and sampling dates had no significant effect (0.06≤P≤0.96), hence data were combined for the analysis. The simpler statistical linear regression (SLR) and polynomial regression (PR), multiple linear regression as well as the advanced machine learning models (support vector machine (SVM), random forest (RF)) tested with color scheme inputs (RGB, DGCI, range pixel count (RPC) of DGCI, and ‘Both’ (RPC + RGB)) produced the best chlorophyll prediction with DGCI, RPC, and ‘Both’ inputs (0.87<R2<0.89; 2.90≤RMSE≤3.41 SPAD units). Overall, these models were not significantly different, but the SVM model found to be the best (R2=0.89 and RMSE=2.90SPAD units). The simpler SLR and PR models with DGCI input (R2≥0.87 and RMSE≤3.1 SPAD units) performed as good as the advanced SVM and RF models. The SVM model had the potential of predicting the chlorophyll directly with the raw RGB input (R2=0.86 and RMSE=3.20SPAD units) without the need of using the standard calibration board. The developed methodology of image processing with machine learning modeling and conversion relationship of measuring infield soybean leaf chlorophyll is efficient, inexpensive, not requiring the standard calibration board, and can be easily extended to other large-scale aerial imaging platforms and field crops.

Leaf chlorophyll estimates of temperate deciduous shrubs during autumn senescence using a SPAD-502 meter and calibration with extracted chlorophyll

The soil plant analysis development (SPAD) meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. However, due to the change in the relationship between SPAD values and Chl content during the course of autumn senescence, it may be necessary to establish separate calibration equations to cover a range of leaf Chl concentrations. Non-destructive estimates of leaf chlorophyll (Chl) content by hand-held chlorophyll meters such as the Minolta SPAD-502 provide an effective time-efficient method of collecting field data on senescence. To establish useable species-specific calibration equations of relationships between chlorophyll meter readings and leaf Chl content. During autumn, we collected SPAD values and quantum yield of photosystem-II (ΦPSII) over a 10-week period from 1044 leaf samples representing native and non-native temperate deciduous shrub species growing in the wild. Subsequently, Chl was extracted from leaf discs (1 cm diameter), incubated at 65 °C for 2 h in dimethyl sulfoxide, and quantified by spectroscopy. Relationships between extracted Chl and SPAD values were established using linear, quadratic, and exponential equations. Quadratic functions proved the most reliable fit for the data. Interestingly, non-native species tended to have higher leaf Chl content and SPAD values but also exhibited higher variability than native species. The strength (r2) of the SPAD-Chl relationships was weaker than those reported for agricultural or greenhouse-grown plants, but strengthened over time as Chl, ΦPSII, and SPAD values declined. The SPAD meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. These estimates may be used in future to validate remote sensing (satellite and near-surface) vegetation indices in late autumn when trees are leafless.

Biochemical, Physiological and Yield Characteristics of Red Basil as Affected by Cultivar and Fertilization

Unconventional fertilizers can act as elicitors to encourage the synthesis of phyto-pharmaceuticals in aromatic plants. In the present research, the effects of factorial combination between two red basil cultivars, ‘Opal’ and ‘De Buzau’, and four fertilization types, biosolids, organic, microorganisms and chemical, plus an unfertilized control, were assessed on fresh and dry yield, biometrical parameters, soil plant analysis development (SPAD) and antioxidant compounds and activity. Chemical fertilization increased fresh yield compared with the control, with no difference in organic and microorganism fertilization regarding dry weight. ‘De Buzau’ enhanced the number of lateral stems and plant height, the latter being better affected by chemical and microorganisms compared to the control. Chemical fertilization showed the highest leaf dry matter, nitrate content and SPAD, whereas the control showed the lowest. Compared to the unfertilized control, biosolids increased total phenolics in ‘Opal’; microorganisms, organic and biosolids enhanced total flavonoids in ‘Opal’, with the same effect under microorganisms and organic treatments in ‘De Buzau’. Total anthocyanins showed the highest content in ‘Opal’ under organic fertilization. The highest antioxidant activity in the basil extracts was detected under microorganisms and organic applications in ‘Opal’. The present investigation results demonstrate that unconventional fertilizers increase the synthesis of antioxidants and represent a sustainable alternative to chemical fertilization for growing red basil.

Nitrogen Status Assessment for Multiple Cultivars of Strawberries Using Portable FT-NIR Spectrometers Combined With Cultivar Recognition and Multivariate Analysis

The nondestructive assessment of nitrogen status in strawberry was performed to analyze its growth and provide guidance for precise management of N fertilizer using Fourier transform near-infrared reflectance (FT-NIR) spectroscopy with leaf spectral reflectance. The leaf soil plant analysis development (SPAD) value is considered as an indicator that indirectly reflects the nitrogen status in strawberry. However, the variation of cultivars leads to differences in the cell structure and light scattering and/or reflection effects of strawberry leaves during strawberry N status assessment, which means, leaf SPAD threshold value and the specific N demand will vary with different cultivars. As a result, accurate measurement of SPAD values and N status in strawberries with multiple cultivars is still challenging. In this study, the individual-cultivar, hybrid-cultivar, and multi-cultivar models were developed for the determination of SPAD values, and the performance of the models in lessening the impact of cultivar variation was studied and compared. The individual-cultivar model was constructed on the basis of a single cultivar of strawberry leaf. The hybrid-cultivar model was developed by merging the spectrum reflectance data and SPAD values of all studied leaf samples, and a multi-cultivar model was built in combination with cultivar identification, individual-cultivar models, and model search strategy. The results indicated that the multi-cultivar model was superior to the other two models for SPAD value estimation of strawberry leaves from different cultivars, with the overall Rp and RMSEP values of 0.966 and 0.468, respectively. We demonstrate that the leaf N content of strawberry is profoundly affected by cultivar variation, and establishing a multi-cultivar model is useful in monitoring the nitrogen status and guiding N fertilization recommendations for different strawberry cultivars.

Iron oxide‐humic acid coprecipitates as iron source for cucumber plants

AbstractIn soil, iron (Fe) solubility depends on complex interactions between Fe minerals and organic matter, but very little is known about plant availability of Fe present in Fe oxides associated with humic substances. For this purpose, this study investigates the effect of Fe mineral crystallinity in the presence of humic acids (HA) on Fe availability to plants. Four Fe–HA mineral coprecipitates were prepared, either in the presence or absence of oxygen, i.e., two goethite (G)‐HA samples containing large amounts of Fe as nanocrystalline goethite and ferrihydrite mixed phases, and two magnetite (M)‐HA samples containing crystalline magnetite. Bioavailability studies were conducted in hydroponic systems on cucumber plants (Cucumis sativus L.) grown under Fe deficient conditions and supplied with the Fe–HA coprecipitates containing goethite or magnetite. Results showed that plants grown in the presence of Fe–HA coprecipitates exhibited a complete recovery from Fe deficiency, albeit less efficiently than plants resupplied with Fe‐chelate fertilizer used as control (Fe‐diethylene triamine penta acetic acid, Fe‐DTPA). However, the supply with either G‐ or M–HA coprecipitates produced different effects on plants: G–HA‐treated plants showed a higher Fe content in leaves, while M–HA‐treated plants displayed a higher leaf biomass and SPAD (Soil–Plant Analysis Development) index recovery, as compared to Fe‐DTPA. The distribution of macronutrients in the leaves, as imaged by micro X‐ray fluorescence (µXRF) spectroscopy, was different in G–HA and M–HA‐treated plants. In particular, plants supplied with the poorly crystalline G–HA coprecipitate with a lower Fe/HA ratio showed features more similar to those of fully recovered plants (supplied with Fe‐DTPA). These results highlight the importance of mineral crystallinity of Fe–HA coprecipitates on Fe bioavailability and Fe uptake in hydroponic experiments. In addition, the present data demonstrate that cucumber plants can efficiently mobilize Fe, even from goethite and ferrihydrite mixed phases and magnetite, which are usually considered unavailable for plant nutrition.