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Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses

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Published/Copyright: August 24, 2024
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Open Agriculture
From the journal Open Agriculture Volume 9 Issue 1

Abstract

Excess nitrogen fertilizer use leads to vegetables with high amounts of nitrate content. Consumption of vegetables with high amounts of nitrate is carcinogenic to human beings. In this study, fenugreek plants were grown under varying nitrogen fertilizer doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kg N/ha). A Vis-NIR hyperspectral imaging (HIS) camera captured images of fenugreek leaves within the 398–1,003 nm spectral range. The spectral data were pre-processed using different pre-processing techniques before the model development. Partial least-squares regression (PLSR) models were constructed with complete spectral data and selected wavelengths. The performance of the PLSR model decreased with pre-processed spectral data, and there was no significant difference compared to the model constructed with raw spectral data (R 2 CV = 0.915, SECV = 591.933, slope = 0.518 and RPDCV = 1.421). The wavelengths 411, 435, 466, 558, 669, and 720 nm were selected as feature wavelengths for representing nitrate content in fenugreek leaves. The performance of the PLSR model constructed with feature wavelengths (SECV = 648.672; RPDCV = 1.482; R 2 CV = 0.869) was non-significant compared with the model developed with raw complete spectral data (SECV = 591.933; R 2 CV = 0.915 and RPDCV = 1.421). Using the complete raw spectral data, the spatial distribution images of nitrate content in fenugreek leaves indicated that the nitrate content was concentrated near and along the midrib up to the apex. The overall results obtained in the present study suggest that VIS-NIR HSI, along with suitable chemometric techniques, can be used for rapid assessment of nitrate content in fenugreek leaves.

Keywords: nitrogenous fertilizer; nitrate; fenugreek leaves; hyperspectral imaging; PLSR; food safety; non-destructive; chemometrics

1 Introduction

Nitrogen (N) is one of the vital elements in plants after carbon (C) and hydrogen (H); it plays a crucial role in cell growth, plant development, metabolism, and resource allocation [1]. Although the atmosphere of the Earth comprises 78% N by volume, in this form, it cannot be useful for plants [2]. Plants can absorb nitrogen as minerals from the soil; therefore, the nitrogen present in the atmosphere should be converted into an oxidized state such as nitrite (NO2 −) and nitrate (NO3 −), and the reduced state as ammonium (NH4 +) [3]. The supply of N from the soil is limited, which drives farmers to apply excess nitrogenous fertilizers for better yields [4]. Inorganic fertilizers are more accepted by farmers than organic fertilizers due to their lower cost, ease of availability, and rapid source of plant nutrients, resulting in a spontaneous intended effect. The accumulation of nitrate in vegetables depends on several factors, such as soil nitrogen content, form and dose of nitrogenous fertilizer, temperature, and genetic makeup of the plant [5,6]. Human intestinal microfauna converts the nitrate present in vegetables into nitrites. Nitrates may be considered as the index or the precursor to the amount of nitrite that may be formed in the human body. These nitrites are responsible for the formation of carcinogenic N-nitroso compounds and may lead to methemoglobinemia [7]. The potential hazard may be increased if foods contain high nitrate levels and the storage or processing conditions are conducive to converting nitrates to nitrites. There is widespread concern about the nitrate we consume through vegetables, and the concern exists that it might harm human health [8]. The daily uptake of nitrate content by the humans is restricted to 0–3.7 mg/kg body as per the World Health Organization (WHO) standards; however, this limit has been redefined as 7.0 mg/kg body weight per day by the U.S. Environmental Protection Agency (EPA) [5].

Fenugreek (Trigonella foenum-graecum) is a leguminous plant that belongs to the Fabaceae family. The leaves and seeds of this plant have been used as medicinal, spice, and flavoring agents during food cooking since ancient times [9]. Fenugreek is a rich source of protein (4.4%), minerals (Ca, Zn, and Fe), vitamins (B1, B2, B3, carotene, and C), fiber (1.1%), carbohydrates (6%), bioactive compounds (rhaponticin and isovitexin), alkaloids (trigonolline, cholin, gentianine, carpaine, etc.), and antioxidants (flavonoids and phenolics) [10,11]. The fenugreek leaves are often consumed in raw form (salad and smoothies), cooked (curries, steamed, soups), boiled, culinary, frozen or dehydrated, coloring agent and food fortifying agent in chapatti (unleavened bread), biscuits, and extruded products [12,13].

The traditional laboratory nitrate measurement techniques such as HPLC, ion chromatography, spectrophotometer, biosensors, selective ion electrodes, and SAP Merkoquant test strips required sample preparation, a skilled workforce, time intensive, cost-intensive, and destructive [14,15]. Spectroscopy is one of the techniques accepted by the food industries for food quality and safety monitoring due to its low cost, rapid detection, and non-destructive measurement technique [16]. However, the spectra measurements do not produce the spatial variation of compounds within the commodity. Hyperspectral imaging (HSI) technique is an emerging technology that integrates imaging and spectroscopy techniques into one system. It provides both spatial and spectral information. HSI is a useful tool to measure a spectrum at each pixel in digital images three-dimensionally [7]. The HSI system is a non-destructive technique for assessing quality attributes, consequently facilitating nitrate identification in the most straightforward and faster manner. The present study aims to develop protocols for assessing nitrate content and to check the potential of visible near-infrared (VIS-NIR) HSI in harvested fenugreek leaves. The objectives of the present study are as follows: (a) the development of partial least-squares regression (PLSR) models using the spectral data; (b) to investigate the effect of various spectral pre-processing methods on the model prediction accuracy; (c) identification of featured wavelengths associated with the depiction of nitrate content and development of PLSR model with selected wavelengths; and (d) generation of spatial distribution images of nitrate content within the leaves grown under different levels of nitrogen fertilizer doses.

2 Materials and methods

2.1 Sample preparation

Fenugreek was grown in plastic bags of size 1 m × 1 m × 0.3 m from December to February in the open environment at the farm (23°18′57.6″N 77°24′14.3″E) of ICAR-CIAE, Bhopal, India. The bags were filled up to a depth of 20 cm with soil collected from the same field (Figure 1). The application rate of fertilizers P and K was fixed as 60 and 40 kg/ha, respectively. The plants were administered with different levels of nitrogen: 0, 50, 100, 150, 200, 250, 300, 350, and 400 kg N/ha. The inorganic fertilizers N, P, and K were applied to the plant in the form of urea (CO(NH2)2) (46%, N), 1-super phosphate (16%, P), and muriate of potash (MOP) (60%, K), respectively. The whole P and K were applied at the time of sowing; however, 50% N was applied at the time of sowing, and the remaining 50% N was applied in two stages: 25% after 15 days and the remaining 25% after 25 days to increase the uptake efficiency. The plants were irrigated with the help of watering cans three times a week.

Figure 1 Cultivation of fenugreek under varying nitrogen fertilizer doses. Source: Picture taken during experiment.
Figure 1

Cultivation of fenugreek under varying nitrogen fertilizer doses. Source: Picture taken during experiment.

2.2 HSI

The fenugreek leaves were collected after 35 days of sowing during the early hours of the day, followed by immediate capture of a hyperspectral image of the adaxial side of the leaves. A total of 270 fenugreek leaves were collected for analysis, with 30 leaves from each treatment. Hyperspectral images of fenugreek leaves grown under different nitrogen fertilizer doses were acquired using a push-broom VIS-NIR HSI (398–1,003 nm) system (OLES30, Specim, Oulu, Finland) with a spectral resolution of 6.24 nm in a reflectance mode (Figure 2).

Figure 2 Acquisition of hyperspectral images of fenugreek leaves. Source: Picture taken during experiment.
Figure 2

Acquisition of hyperspectral images of fenugreek leaves. Source: Picture taken during experiment.

2.2.1 Image acquisition and calibration

Images were acquired in the machine vision laboratory of the institute under three tungsten–halogen bulbs (50 W each) perched at an angle of 45° to the platform. A raw HSI is influenced by imminent optical noise unevenly distributed within the various wavebands. The raw hyperspectral images were corrected before using for any further analysis. The black reference image (D ref) was acquired by completely turning off the light, and the image of the white Teflon plate (99.9% reflectance) was acquired as a white reference (W ref) under the same conditions as it was during the capture of the raw image (R image) [17]. The relative reflectance (R) of the hypercube was calculated using the following equation:

(1) R = R image − D ref W ref − D ref × 100 .

2.2.2 Pre-processing of hyperspectral images

The acquired hyperspectral images were pre-processed before using for further analysis. The hyperspectral images were spatially cropped in order to acquire the area covered by the leaves alone. Thereafter, the presence of dead pixels and spikes was investigated. Further, the background of the sample was removed by employing K-means clustering with two clusters (Figure 3). The first cluster (brown color, Figure 3a) represents the background, and the second cluster (blue color, Figure 3a) represents the fenugreek leaves. The fenugreek leaves without background are illustrated in Figure 3b. The spurious pixels were eliminated manually; after that, the spectral data of all the pixels in each leaf belonging to different treatments were obtained separately, and mean spectra were calculated for each treatment. In all, 270 spectral reflectance values were obtained for the development of the model.

Figure 3 Morphological operations of hyperspectral images: (a) k-means clustering and (b) fenugreek leaves with eliminated background.
Figure 3

Morphological operations of hyperspectral images: (a) k-means clustering and (b) fenugreek leaves with eliminated background.

2.2.3 Pre-processing of spectral data

The primary objective of spectral pre-processing is to rectify the influence of factors that affect the spectral data coming for measurement, such as scattering of light, morphology of product, size of particles, surface roughness, and instrumental noise, and to enhance the deviation among or between the spectral data for further analysis [18,19]. In this study, the spectral data were pre-processed before model development using scatter correction techniques such as MSC (multiplicate scatter correction), SNV (standard normal variate), and MSC + SNV; derivative techniques (1st and 2nd derivatives); smoothing technique (Savitzky-Golay-SG) filter smoothing, window-7, degree-2) and combination of scatter and smoothing techniques (SNV + SG smoothing-window-7, degree-2). Scatter correction techniques such as MSC and SNV removed the scattering in raw spectral data induced by the illumination source. However, the trends in raw spectral data were removed after pre-processing of spectral data with the de-trending technique. The spectral noise in raw spectral data was removed and smoothened, and derivative techniques such as SG-smoothing enhanced the spectra’s difference. The selection of a window size of five resulted in noise generation. As a result, the performance of models decreased. However, a window size of seven was optimal for pre-processing raw spectral data while retaining valuable information.

2.3 Nitrate estimation

After obtaining the spectral data, the leaves were immediately weighed using a digital weighing balance and transferred into test tubes containing 2 ml of distilled water. The test tubes were closed with three layers of aluminum foil to avoid the evaporation of water and then boiled for 20 min in a hot water bath [7]. These test tubes were cooled to room temperature, and the nitrate content was estimated using a nitrate meter (HORIBA LAQUA twin Nitrate meter, Spectrum Technologies, Inc., Aurora, IL, USA). The nitrate meter worked on the principle of the ion-selective conductive method with a measurement range of 16–4,000 ppm (mg of NO3 −/L), with an accuracy of ±10%. The nitrate meter was calibrated using 150 and 2,000 ppm standard solutions. The accuracy of the nitrate meter was cross-checked by preparing different concentrations of nitrate solution using potassium nitrate. The following equation was used to calculate the nitrate content of the sample [7]:

(2) NC S = NC l W s × V l ,

where NCS is the nitrate content of the sample, mg/kg; NCl is the nitrate concentration in the extract, mg/mL; W s is the weight of the leaf, kg; and V l is the volume of the extraction liquid, mL.

2.4 PLSR

PLSR is a generalization of the multi-linear regression model. It is an advanced technique that combines both the features of principal component analysis and regression analysis. A PLSR model can effectively handle the limitation of collinearity and overlapping of bands with spectral data. This technique is suitable where independent variables are higher than dependent ones [20,21]. PLSR tries to maximize the covariance, thus capturing the variance and correlating the data together [22]. The basic principle behind the PLSR is to extract the latent variables (LVs) accounting for as much of the marked factor variation as possible while it models the responses [23]. PLSR decomposes the X and Y data into scores and loadings before model development. The PLSR models were developed to assess the nitrate content of fenugreek leaves using the spectral data obtained from the HSI system and the nitrate content obtained by the destructive analysis. The whole samples (270) were divided into two sets in the ratio of 70:30 for model development and testing, respectively. The developed model was internally cross-validated using the Venetian blind cross-validation approach with five groups.

2.5 Statistical assessment of models

The statistical parameters such as bias, coefficient of determination (R 2), slope, standard error (SE), and residual predictive deviation (RPD) are used as tools for the evaluation of model performance obtained during cross-validation. The RPD is the ratio between standard deviation (SD) and SE. The RPD (ratio of performance to deviation) is used to evaluate a model’s effectiveness. The RPD value indicates the model’s performance level. If the RPD value is greater than 2.5, the model is excellent. The model is very good if the value falls between 2.0 and 2.5. A value between 1.8 and 2.0 indicates a good model, whereas a value between 1.4 and 1.8 is considered fair. If the value is between 1 and 1.4, the model is poor, and if it is below 1, the model cannot be applied [24]:

(3) R 2 = ∑ ( Y P − Y P ̅ ) ( Y O − Y O ̅ ) 2 ∑ ( Y P − Y P ̅ ) 2 ∑ ( Y O − Y O ̅ ) 2 ,

(4) SE = SD n ,

(5) SD = ∑ i = 1 N ( Y i − Y ̅ ) 2 n − 1 ,

(6) bias = ∑ ( Y O − Y P ) n ,

(7) RPD CV = SD SE CV ,

where Y O is the actual measured value, Y P is the predicted value by the model, SD is the standard deviation, and n is the number of samples. Fisher’s F test was adopted to determine the statistical significance (p < 0.05) between the statistical parameters (SECV and RPDCV) of the models constructed with raw and pre-processed spectral data [25]. The following equation was used to calculate the F value:

(8) F = ( SE CV 2 ) 2 ( SE CV 1 ) 2 ,

where SECV1 and SECV2 are standard errors of different models constructed with unprocessed and pre-processed spectral data obtained during model cross-validation, respectively, and SECV2 > SECV1. F is compared with F critical (1 − P, n 2 − 1, n 1 − 1) read from the F table with p = 0.05 and n − 1 degree of freedom. If F is higher than the F critical, then the two SECV values are considered significantly different.

2.5.1 Software and system configurations

HSI was corrected using customized code developed in MATLAB 2018a® software. The spectral processing and model development was carried out using MATLAB-based graphical user interface (GUI) HYPERTOOLS v.2, developed by Mobaraki and Amigo [26]. Image acquisition and analysis were carried out on a desktop computer with a Windows 10 operating system, Intel (R) Core i7-7700 CPU @ 360 GHz processor, 64-bit operating system, and 8 GB RAM.

3 Results and discussion

3.1 Population characterization

The nitrate content of fenugreek leaves varied between 607 and 4,230 mg/kg. The descriptive statistical parameters, such as the number of samples (N), mean, range, and SD of training and validation sets, are shown in Figure 4. It was observed that both testing and validation tests have a similar range, SD, and mean. As a result, the sampling technique adopted can potentially characterize samples into training and testing sets effectively. If the distribution of the samples in the training and testing tests is improper, it may lead to over or under-fitting of the model and result in poor performance. By analyzing the frequency distribution images of nitrate content used for training and testing a model for fenugreek leaves, it was found that most of the leaves had nitrate (mg/kg) content within the range of 1,000−1,200 mg/kg.

Figure 4 Distribution of nitrate content of fenugreek leaves in (a) training set and (b) validation data set.
Figure 4

Distribution of nitrate content of fenugreek leaves in (a) training set and (b) validation data set.

3.2 Characterization of spectral signature

All the fenugreek leaves grown under the different levels of nitrogen doses have depicted the spectral signature of a similar trend irrespective of the leaf nitrate content (Figure 5). The spectral reflectance values of fenugreek leaves decreased with an increase in nitrate content. The compounds present in the leaves, such as chlorophyll, starches, xanthophylls, sugars, lignin, cellulose, and proteins, are responsible for the spectral absorption features of the fenugreek leaves [27].

Figure 5 Spectral signature of fenugreek leaves.
Figure 5

Spectral signature of fenugreek leaves.

The lower reflectance values within the visible region (400−700 nm) were due to the absorption characteristics of chlorophyll. The maximum absorption for chlorophyll-a occurs within the spectral regions of 410–430 nm (blue) and 600−690 nm (red). Similarly, for chlorophyll-b, the maximum absorption occurs within the wavelength range of 450−470 nm [28]. The high reflectance values of fenugreek leaves within the spectral range 700−1,000 nm were due to the pigments present in the leaves being almost translucent in this spectral region, so the reflectance values were higher in this region [27]. The spectral data at the starting (398−405 nm) and end (995−1,003 nm) of the spectrum were not considered for the analysis as the spectra were jagged and the spectral data in this region has a very low signal-to-noise ratio.

3.3 PLSR models with whole spectral data

The PLSR models were constructed with raw and pre-processed hyperspectral data to estimate the nitrate concentration in the fenugreek leaves. The optimum LVs were selected from the graph based on the cumulative explained variance and the number of factors. The optimum LVs and statistical results of PLSR models constructed with raw and pre-processed spectral data for fenugreek leaves during calibration (C), cross-validation (CV), and testing (P) are tabulated in Table 1. The optimum number of LVs for raw, SG-125, and SG-127 pre-processing techniques was four; for MSC and SNV pre-processing techniques, it was three; and for de-trend and baseline correction, it was two. The R 2, SE, slope, and RPD values during model CV varied in the ranges 0.851–0.915, 591.933–781.682, 0.169–0.518, and 1.076–1.421, respectively.

Table 1

Calibration (C), cross-validation (CV), and prediction (P) statistics for PLS developed using different pre-processed HSI data for predicting nitrate content in harvested fenugreek leaves

Pre-processing LV Slope Offset R 2 SE Bias RPD SEL
Raw 4 C 0.545 838.281 0.923 563.02 2.442 1.494 178.12 mg/kg
CV 0.518 891.306 0.915 591.933 5.541 1.421
T 0.630 691.389 0.934 532.308 −0.624 1.641
SNV 3 C 0.301 1284.38 0.88 702.963 −0.0507 1.196
CV 0.28 1328.1 0.873 723.916 3.497 1.162
T 0.437 1052.34 0.9 655.151 −0.056 1.333
MSC 3 C 0.301 1284.26 0.88 402.85 0.001 1.186
CV 0.28 1327.95 0.872 723.765 3.56 1.162
T 0.437 1052.22 0.9 655.019 −0.001 1.333
Detrend 2 C 0.188 1489.3 0.858 763.421* −4.461 1.102
CV 0.169 1518.89 0.851 781.682* −8.967 1.076
T 0.347 1216.45 0.883 711.008 −4.144 1.228
Baseline 2 C 0.221 1428.69 0.864 745.824* −3.003 1.123
CV 0.211 1448.11 0.862 752.89* −3.097 1.117
T 0.29 1322.91 0.872 741.653 −4.625 1.178
SG-125 4 C 0.38 1137.11 0.892 667.229 −3.498 1.260
CV 0.348 1197.09 0.882 696.169 −2.743 1.208
T 0.465 999.929 0.905 640.792 −1.256 1.363
SG-127 4 C 0.366 1162.7 0.889 674.624 −3.533 1.247
CV 0.344 1206.27 0.882 696.708 −0.472 1.207
T 0.478 975.773 0.908 630.762 0.006 1.385
Selected wavelengths 3 C 0.450 1007.298 0.903 633.687 −6.535 1.327
CV 0.444 1019.114 0.898 648.079 −4.625 1.348
T 0.454 1002.608 0.895 672.162 −18.830 1.299

*Significant difference (p < 0.05) in values as compared to raw.

Bold value represents the level of significance (5%) was reported.

The statistical results revealed that the model performance decreased after pre-processing spectral data. Spectral pre-processing does not significantly affect model performance, except for de-trending and baseline correction. The PLSR models developed with raw (R 2 CV = 0.915; SECV = 591.933; slope = 0.518, and RPDCV = 1.421) hyperspectral data depicted a good prediction accuracy over other pre-processing techniques. The plot between actual and predicted nitrate content of fenugreek leaves during models C and CV is shown in Figure 6a. The de-trending (R 2 CV = 0.851; SECV = 781.682; slope = 0.169, and RPDCV = 1.076) pre-processing technique reported poor performance compared to other pre-processing techniques.

Figure 6 Reference versus predicted nitrate content for the PLS model developed with HSI raw full spectra of fenugreek leaves: (a) calibration (blue) and cross-validation (red), (b) validation, and (c) selection of featured wavelengths. *The black line indicates the original regression line.
Figure 6

Reference versus predicted nitrate content for the PLS model developed with HSI raw full spectra of fenugreek leaves: (a) calibration (blue) and cross-validation (red), (b) validation, and (c) selection of featured wavelengths. *The black line indicates the original regression line.

The results obtained in this study are in accordance with the results stated by some of the earlier studies, where it has been claimed that spectral pre-processing techniques such as MSC [29], derivative [30], and SNV [31] do not improve the model performance. Cao et al. [32] observed that the performance of a model developed without any spectral pre-processing technique yields better results. Similarly, the raw spectral data were used for the development of prediction models to predict total nitrogen in pepper plants [33], pigments in cucumber leaves [34], and nitrogen concentration in rapeseed leaves [35].

The pre-processing of spectral data results in a decrease in slope and RPD values. The RPD value of the developed models with raw spectral data was greater than 1.4, so the developed models can be classified as “Fair.” However, after pre-processing of spectral data, the RPD values of PLSR models fell below 1.4, so these models are classified as “Poor.” The slope of the PLSR model constructed with raw spectral data was 0.52, higher than the model developed with other pre-processing techniques. The results reported in the present study were better compared to the results of Yang et al. [36] for spinach (R 2 = 0.74; RMSE = 710.16) and Itoh et al. [7] (R 2 = 0.870; weighted average of standard error [WSE] = 1,446 ppm). Yu et al. [35] developed different regression models (PLSR, LS-SVM, SAE-FNN stacked autoencoders, and fully connected neural networks) to predict the total nitrogen content in rapeseed leaves. This model’s performance was superior compared to the present study; it can be attributed to the low SD of regression data, considering that the RPD depends on the SD of the regression data. Zhao et al. (2016) developed PLSR models to predict cucumber leaves’ pigments (chlorophyll and carotenoids). The performance of the model was depleted (R 2 chl = 0.894; R 2 car = 0.787; RMSEchl = 0.347; RMSEcar = 0.081) vis-à-vis the present study; again, the SD was lower (Chl-0.776; Car-0.133) compared to the present study. Similarly, Liu et al. [37] developed PLSR models to predict nitrogen and phosphorus in citrus leaves with prediction accuracy lower than the model performance of the present study. However, the results reported by Yu et al. [33] for the assessment of total nitrogen content in pepper plants were slightly better than the present study (R 2 = 0.798–0.931; RMSE = 0.064–0.420%), again owing to the very low range and SD of the data.

All the constructed models with raw and pre-processed data met the required validation requirements (R 2 T > 0.85). The R 2 T, SET, and RPDT values for fenugreek leaves varied in the ranges 0.872–0.928, 554.127–737.075, 557.568–741.653, and 1.178–1.566, respectively. The plot between actual and estimated nitrate content during model validation is shown in Figure 6b. In general, the SET is a useful tool to evaluate the predictive capability of a model; if it is two times SEL, then the model can be considered an excellent model. In this study, the SET (532.308) was almost three times the SEL (178.12), which implies that the model performance is within the acceptable limit [38]. The nitrate concentration within the leaf is asymmetric, but while analyzing the nitrate content, the whole leaf liquefied; therefore, the sampling error was not included in the SEL value.

3.4 Selection of featured wavelengths

The model constructed with raw spectral data was considered optimal; therefore, the regression coefficients obtained from this model were used to select the featured wavelengths. The plot between regression coefficients and corresponding wavelengths for fenugreek leaves is shown in Figure 6c. The wavelengths 411, 435, 466, 558, 669, and 720 nm were considered featured wavelengths accountable for nitrate content in fenugreek leaves. These wavelengths were considered for PLSR model construction to predict the nitrate in fenugreek leaves. The wavelengths 411, 435, and 669 nm were responsible for the chlorophyll-a content, whereas the wavelengths 466, 558, and 720 nm were associated with the chlorophyll-b content. According to Sahoo et al. [39], the absorption characteristics within the wave bands 450–485 nm and 625–740 nm were due to the chlorophyll content. There is a strong correlation between nitrate and chlorophyll content of the leaf. Therefore, the spectral features of chlorophyll content were found to be associated with nitrate content [40,41,42].

3.5 PLSR model with featured wavelengths

A new model was constructed with the featured wavelengths, and its efficacy was evaluated in estimating nitrate content in fenugreek leaves. The number of wavelengths (variables for the model) was reduced from 97 to 6 (411, 435, 466, 558, 669, and 720 nm). The PLSR models were developed with effective wavelengths to compute nitrate concentration in fenugreek leaves with three optimum LVs. The statistical results acquired from the new PLSR model during C, CV, and T are tabulated in Table 1. The performance of the developed model (SECV = 648.672; RPDCV = 1.482; R 2 CV = 0.869) is non-significant when compared with the PLSR model constructed with whole raw spectral data (SECV = 591.933, R 2 CV = 0.915, and RPDCV = 1.421). The performance of the developed models met the required prediction accuracy (R 2 P > 0.85), but the slope of the regression line decreased compared with the model constructed with complete hyperspectral data. The graph between the reference and estimated nitrate content of fenugreek leaves during model C and CV is illustrated in Figure 7a. The blue and red lines designate regression lines of a model during C and CV, and the black line signifies the targeted regression line. The developed model was tested with an external data set, and the statistical results are listed in Table 1. From the results obtained during model testing, it is clear that the statistical results obtained during model testing (SET = 648.079; RPDT = 1.348; R 2 T = 0.898) are comparable to the results obtained during cross-validation (SECV = 648.672; RPDCV = 1.482; R 2 CV = 0.869), which implies the developed models are free from over- or under-fitting. The graph between the actual and estimated nitrate content during the model testing is depicted in Figure 7b. The model developed with featured wavelengths had unique benefits over the model development with whole spectral data in terms of high computational speed and less complexity due to fewer variables.

Figure 7 PLS model with selected wavelengths; reference versus predicted nitrate content: (a) calibration (blue) and cross-validation (red) and (b) validation. *The black line indicates the original regression line.
Figure 7

PLS model with selected wavelengths; reference versus predicted nitrate content: (a) calibration (blue) and cross-validation (red) and (b) validation. *The black line indicates the original regression line.

3.6 Spatial distribution of nitrate in fenugreek leaves

The performance of PLSR models developed with complete raw spectral data was far superior. Hence, that same was used for the fenugreek leaves of all treatments to generate the spatial concentration distribution of nitrate content within the leaf. The spatial distribution of nitrate content within the fenugreek leaves for all treatments is shown in Figure 8. The nitrate content of fenugreek leaves was not uniform throughout the leaf. The nitrate content of fenugreek leaves varies from 900 to 2,300 mg/kg. The nitrate content of fenugreek leaves increased with an increase in nitrogen fertilizer dose and was found to be concentrated in and around the midrib of the leaf. Also, the nitrate content at the leaf apex area was higher than the area around the petiole. Similar spatial distribution patterns of nitrogen content were reported for the pepper plants [33] and spinach leaves [36].

Figure 8 Spatial distribution of nitrate content (mg/kg) in fenugreek leaves.
Figure 8

Spatial distribution of nitrate content (mg/kg) in fenugreek leaves.

4 Conclusion

The results obtained in this study imply that the nitrate content in fenugreek leaves can be assessed rapidly by the application of VIS-NIR HSI with suitable chemometric techniques. The PLSR model developed with whole raw spectral data depicted good prediction accuracy (R 2 CV = 0.915, SECV = 591.933, slope = 0.518, and RPDCV = 1.421). The performance of the PLSR model decreased with pre-processed spectral data, and there was no significant statistical difference upon comparison with the model developed with raw spectral data. The wavelengths 411, 435, 466, 558, 669, and 720 nm were considered significant wavelengths representing nitrate content in fenugreek leaves. No significant difference in statistical results was observed between the models developed with feature wavelengths (SECV = 648.672; RPDCV = 1.482; R 2 CV = 0.869) and whole spectral data (R 2 CV = 0.915, SECV = 591.933, and RPDCV = 1.421). The spread of nitrate on the leaf lamina was asymmetric. It decreased gradually from the midrib to the periphery of the leaf lamina. The results obtained in the present study affirmed that the VIS-NIR HSI system, in conjunction with suitable chemometric techniques, could be used for rapid and non-destructive assessment of nitrate content in fenugreek leaves.

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Acknowledgements

The authors express deepest gratitude to the Indian Agricultural Research Institute (IARI), New Delhi, and Central Institute of Agricultural Engineering (CIAE), Bhopal, for providing the essential technical support and resources for the successful completion of this work.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. NM: conceptualization, methodology, data curation, software, and writing – original draft. SC: conceptualization, methodology, visualization, supervision, and writing – review and editing. PP: methodology, formal analysis, validation, and writing – review and editing.

  3. Conflict of interest: Pankaj B. Pathare is an Editor of Open Agriculture (Agricultural Engineering). Authors state no conflict of interest.

  4. Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

[1] Watts DB, Torbert HA, Prior SA, Huluka G. Long-term tillage and poultry litter impacts soil carbon and nitrogen mineralization and fertility. Soil Sci Soc Am J. 2010;74:1239–47.10.2136/sssaj2008.0415Search in Google Scholar

[2] Ahmed M, Rauf M, Mukhtar Z, Saeed NA. Excessive use of nitrogenous fertilizers: an unawareness causing serious threats to environment and human health. Environ Sci Pollut Res. 2017;24(35):26983–7.10.1007/s11356-017-0589-7Search in Google Scholar PubMed

[3] Taiz L, Zeiger E. Plant physiology. 5th edn. Sunderland, MA, USA: Sinauer Associates Inc; 2010. p. 67–86.Search in Google Scholar

[4] Vigneau N, Ecarnot M, Rabatel G, Roumet P. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. Field Crop Res. 2011;122(1):25–31.10.1016/j.fcr.2011.02.003Search in Google Scholar

[5] Liu CW, Sung Y, Chen BC, Lai HY. Effects of nitrogen fertilizers on the growth and nitrate content of lettuce (Lactuca sativa L.). Int J Environ Res Public Health. 2014;11(4):4427–40.10.3390/ijerph110404427Search in Google Scholar PubMed PubMed Central

[6] Kaymak HC. Effect of nitrogen forms on growth, yield and nitrate accumulation of cultivated purslane (Portulacaoleracea l.). Bulg J Agric Sci. 2013;19(3):444–9.Search in Google Scholar

[7] Itoh H, Kanda S, Matsuura H, Shiraishi N, Sakai K, Sasao A. Measurement of nitrate concentration distribution in vegetables by near-infrared hyperspectral imaging. Environ Control Biol. 2010;48(2):37–49.10.2525/ecb.48.37Search in Google Scholar

[8] Kobayashi J. Effect of diet and gut environment on the gastrointestinal formation of N-nitroso compounds: a review. Nitric Oxide. 2018;73:66–73.10.1016/j.niox.2017.06.001Search in Google Scholar PubMed

[9] Man SM, Păucean A, Călian ID, Mureșan V, Chiș MS, Pop A, et al. Influence of fenugreek flour (Trigonellafoenum-graecum L.) addition on the technofunctional properties of dark wheat flour. J Food Qual. 2019;2019(1):1–8.10.1155/2019/8635806Search in Google Scholar

[10] He Y, Ding C, Wang X, Wang H, Suo Y. Using response surface methodology to optimize countercurrent chromatographic separation of polyphenol compounds from fenugreek (Trigonellafoenum-graecum L.) Seeds. J Liq Chromatogry Relat Technol. 2015;38(1):29–35.10.1080/10826076.2013.864981Search in Google Scholar

[11] Khorshidian N, YousefiAsli M, Arab M, AdeliMirzaie A, Mortazavian AM. Fenugreek: potential applications as a functional food and nutraceutical. Nutr Food Sci Res. 2016;3(1):5–16.10.18869/acadpub.nfsr.3.1.5Search in Google Scholar

[12] Ravindran G, Carr A, Hardacre A. A comparative study of the effects of three galactomannans on the functionality of extruded pea–rice blends. Food Chem. 2011;124(4):1620–6.10.1016/j.foodchem.2010.08.030Search in Google Scholar

[13] Wani SA, Kumar P. Fenugreek: A review on its nutraceutical properties and utilization in various food products. J Saudi Soc Agric Sci. 2018;17(2):97–106.10.1016/j.jssas.2016.01.007Search in Google Scholar

[14] Singh P, Singh MK, Beg YR, Nishad GR. A review on spectroscopic methods for determination of nitrite and nitrate in environmental samples. Talanta. 2019;191:364–81.10.1016/j.talanta.2018.08.028Search in Google Scholar PubMed

[15] Wang QH, Yu LJ, Liu Y, Lin L, Lu RG, Zhu JP, et al. Methods for the detection and determination of nitrite and nitrate: A review. Talanta. 2017;165:709–20.10.1016/j.talanta.2016.12.044Search in Google Scholar PubMed

[16] Ulissi V, Antonucci F, Benincasa P, Farneselli M, Tosti G, Guiducci M, et al. Nitrogen concentration estimation in tomato leaves by VIS-NIR non-destructive spectroscopy. Sensors. 2011;11(6):6411–24.10.3390/s110606411Search in Google Scholar PubMed PubMed Central

[17] Chakraborty SK, Mahanti NK, Mansuri SM, Tripathi MK, Kotwaliwale N, Jayas DS. Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging. J Food Sci Technol. 2021;58:437–50.10.1007/s13197-020-04552-wSearch in Google Scholar PubMed PubMed Central

[18] Vidal M, Amigo JM. Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom Intell Lab Syst. 2012;117:138–48.10.1016/j.chemolab.2012.05.009Search in Google Scholar

[19] Amigo JM, Babamoradi H, Elcoroaristizabal S. Hyperspectral image analysis. A tutorial. Analyticachimicaacta. 2015;896:34–51.10.1016/j.aca.2015.09.030Search in Google Scholar PubMed

[20] Barbin D, Elmasry G, Sun DW, Allen P. Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci. 2012;90(1):259–68.10.1016/j.meatsci.2011.07.011Search in Google Scholar PubMed

[21] Iqbal A, Sun DW, Allen P. Prediction of moisture, color and pH in cooked, pre-sliced Turkey hams by NIR hyperspectral imaging system. J Food Eng. 2013;117(1):42–51.10.1016/j.jfoodeng.2013.02.001Search in Google Scholar

[22] Xiaobo Z, Jiewen Z, Povey MJW, Holmes M, Mao H. Variable selection methods in near-infrared spectroscopy. Analyticachimicaacta. 2010;667(1–2):14–32.10.1016/j.aca.2010.03.048Search in Google Scholar PubMed

[23] Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. USA: Academic press; 2015.Search in Google Scholar

[24] Huang M, Wang Q, Zhang M, Zhu Q. Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. J Food Eng. 2014;128:24–30.10.1016/j.jfoodeng.2013.12.008Search in Google Scholar

[25] Entrenas JA, Pérez-Marín D, Torres I, Garrido-Varo A, Sánchez MT. Simultaneous detection of quality and safety in spinach plants using a new generation of NIRS sensors. Postharvest Biol Technol. 2020;160:111026.10.1016/j.postharvbio.2019.111026Search in Google Scholar

[26] Mobaraki N, Amigo JM. HYPER-Tools. A graphical user-friendly interface for hyperspectral image analysis. Chemom Intell Lab Syst. 2018;172:174–87.10.1016/j.chemolab.2017.11.003Search in Google Scholar

[27] Giovenzana V, Beghi R, Romaniello R, Tamborrino A, Guidetti R, Leone A. Use of visible and near infrared spectroscopy with a view to on-line evaluation of oil content during olive processing. Biosyst Eng. 2018;172:102–9.10.1016/j.biosystemseng.2018.06.001Search in Google Scholar

[28] Neto AJS, Lopes DC, Pinto FA, Zolnier S. Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves. Biosyst Eng. 2017;155:124–33.10.1016/j.biosystemseng.2016.12.008Search in Google Scholar

[29] Herrera J, Guesalaga A, Agosin E. Shortwave–near infrared spectroscopy for non-destructive determination of maturity of wine grapes. Meas Sci Technol. 2003;14:689.10.1088/0957-0233/14/5/320Search in Google Scholar

[30] Larrain M, Guesalaga AR, Agosin E. A multipurpose portable instrument for determining ripeness in wine grapes using NIR spectroscopy. IEEE Trans Instrum Meas. 2008;57:294–302.10.1109/TIM.2007.910098Search in Google Scholar

[31] Fadock M. Non-destructive VIS-NIR reflectance spectrometry for red wine grape analysis. PhD diss. The University of Guelph; 2011.Search in Google Scholar

[32] Cao F, Wu D, He Y. Soluble solids content and pH prediction and varieties discrimination of grapes based on visible–near infrared spectroscopy. Computers Electron Agric. 2010;71:15–8.10.1016/j.compag.2009.05.011Search in Google Scholar

[33] Yu KQ, Zhao YR, Li XL, Shao YN, Liu F, He Y. Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PLoS one. 2014;9(12):e116205.10.1371/journal.pone.0116205Search in Google Scholar PubMed PubMed Central

[34] Zhao YR, Li X, Yu KQ, Cheng F, He Y. Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease. Sci Rep. 2016;6:27790.10.1038/srep27790Search in Google Scholar PubMed PubMed Central

[35] Yu X, Lu H, Liu Q. Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf. Chemom Intell Lab Syst. 2018;172:188–93.10.1016/j.chemolab.2017.12.010Search in Google Scholar

[36] Yang HY, Inagaki T, Ma T, Tsuchikawa S. High-resolution and non-destructive evaluation of the spatial distribution of nitrate and its dynamics in spinach (Spinaciaoleracea L.) Leaves by near-infrared hyperspectral imaging. Front Plant Sci. 2017;8:1937.10.3389/fpls.2017.01937Search in Google Scholar PubMed PubMed Central

[37] Liu Y, Lyu Q, He S, Yi S, Liu X, Xie R, et al. Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Int J Agric Biol Eng. 2015;8(2):80–8.Search in Google Scholar

[38] Sánchez MT, Entrenas JA, Torres I, Vega M, Pérez-Marín D. Monitoring texture and other quality parameters in spinach plants using NIR spectroscopy. Computers Electron Agric. 2018;155:446–52.10.1016/j.compag.2018.11.004Search in Google Scholar

[39] Sahoo RN, Ray SS, Manjunath KR. Hyperspectral remote sensing of agriculture. Curr Sci. 2015;108(5):848–59.Search in Google Scholar

[40] Li L, Lu J, Wang S, Ma Y, Wei Q, Li X, et al. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica napus L.) using in situ leaf spectroscopy. Ind Crop Prod. 2016;91:194–204.10.1016/j.indcrop.2016.07.008Search in Google Scholar

[41] Abdel-Rahman EM, Ahmed FB, Van den Berg M. Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int J Appl Earth Observ Geoinf. 2010;12:52–7.10.1016/j.jag.2009.11.003Search in Google Scholar

[42] Miphokasap P, Honda K, Vaiphasa C, Souris M, Nagai M. Estimating canopy nitrogen concentration in sugarcane using field imaging spectroscopy. Remote Sens. 2012;4(6):1651–70.10.3390/rs4061651Search in Google Scholar
Received: 2023-10-07
Revised: 2024-04-30
Accepted: 2024-08-05
Published Online: 2024-08-24

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0349
Keywords for this article
nitrogenous fertilizer; nitrate; fenugreek leaves; hyperspectral imaging; PLSR; food safety; non-destructive; chemometrics
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L.
  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
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  19. Provision of loans and credit by cocoa buyers under non-price competition: Cocoa beans market in Ghana
  20. Effectiveness of micro-dosing of lime on selected chemical properties of soil in Banja District, North West, Ethiopia
  21. Effect of weather, nitrogen fertilizer, and biostimulators on the root size and yield components of Hordeum vulgare
  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
  30. Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy
  31. Forage yield, seed, and forage qualitative traits evaluation by determining the optimal forage harvesting stage in dual-purpose cultivation in safflower varieties (Carthamus tinctorius L.)
  32. The influence of tourism on the development of urban space: Comparison in Hanoi, Danang, and Ho Chi Minh City
  33. Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia
  34. The role of organic rice farm income on farmer household welfare: Evidence from Yogyakarta, Indonesia
  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
  36. Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia
  37. Factors influencing green, environmentally-friendly consumer behaviour
  38. Factors affecting coffee farmers’ access to financial institutions: The case of Bandung Regency, Indonesia
  39. Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons
  40. Sustainability analysis and decision-making strategy for swamp buffalo (Bubalus bubalis carabauesis) conservation in Jambi Province, Indonesia
  41. Understanding factors affecting rice purchasing decisions in Indonesia: Does rice brand matter?
  42. An implementation of an extended theory of planned behavior to investigate consumer behavior on hygiene sanitation-certified livestock food products
  43. Information technology adoption in Indonesia’s small-scale dairy farms
  44. Draft genome of a biological control agent against Bipolaris sorokiniana, the causal phytopathogen of spot blotch in wheat (Triticum turgidum L. subsp. durum): Bacillus inaquosorum TSO22
  45. Assessment of the recurrent mutagenesis efficacy of sesame crosses followed by isolation and evaluation of promising genetic resources for use in future breeding programs
  46. Fostering cocoa industry resilience: A collaborative approach to managing farm gate price fluctuations in West Sulawesi, Indonesia
  47. Field investigation of component failures for selected farm machinery used in small rice farming operations
  48. Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans
  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
  50. Weather index-based agricultural insurance for flower farmers: Willingness to pay, sales, and profitability perspectives
  51. Meta-analysis of dietary Bacillus spp. on serum biochemical and antioxidant status and egg quality of laying hens
  52. Biochemical characterization of trypsin from Indonesian skipjack tuna (Katsuwonus pelamis) viscera
  53. Determination of C-factor for conventional cultivation and soil conservation technique used in hop gardens
  54. Empowering farmers: Unveiling the economic impacts of contract farming on red chilli farmers’ income in Magelang District, Indonesia
  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
  56. Labor productivity of lowland rice (Oryza sativa L.) farmers in Central Java Province, Indonesia
  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
  59. Effects of dietary Adansonia digitata L. (baobab) seed meal on growth performance and carcass characteristics of broiler chickens: A systematic review and meta-analysis
  60. Analysis and structural characterization of the vid-pisco market
  61. Pseudomonas fluorescens SP007s enhances defense responses against the soybean bacterial pustule caused by Xanthomonas axonopodis pv. glycines
  62. A brief investigation on the prospective of co-composted biochar as a fertilizer for Zucchini plants cultivated in arid sandy soil
  63. Supply chain efficiency of red chilies in the production center of Sleman Indonesia based on performance measurement system
  64. Investment development path for developed economies: Is agriculture different?
  65. Power relations among actors in laying hen business in Indonesia: A MACTOR analysis
  66. High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought
  67. Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions
  68. Structuration, risk management, and institutional dynamics in resolving palm oil conflicts
  69. Spacing strategies for enhancing drought resilience and yield in maize agriculture
  70. Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types
  71. Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation
  72. Yield stability of biofertilizer treatments to soybean in the rainy season based on the GGE biplot
  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
  85. Physical and textural properties and sensory acceptability of wheat bread partially incorporated with unripe non-commercial banana cultivars
  86. Cereibacter sphaeroides ST16 and ST26 were used to solubilize insoluble P forms to improve P uptake, growth, and yield of rice in acidic and extreme saline soil
  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
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