A non-destructive approach in proximal sensing to assess the performance distribution of SPAD prediction models using hyperspectral analysis in apricot trees

Summary

SPAD combined with hyperspectral sensors can be an alternative approach to traditional laboratory methods for determining the physiological status of trees. The aim of this work was to assess whether the effectiveness of SPAD predictive models using hyperspectral data might be influenced by where the measurements were carried out. Leaves of apricot trees of two varieties (Farbaly and Farlis) were analysed with SPAD and spectroradiometer, and the data were organized in two different ways: (i) overall dataset (OD), collecting total measurements of trees in each variety; (ii) subset of overall datasets (SOD), collecting the measurements performed on the cardinal points of trees in each variety. Prediction models were built using as regressors: (i) spectral data transformed with Continuum Removal (CR) methodology (CR indices); (ii) vegetation indices (VI) linked to chlorophyll and nitrogen content; (iii) reflectance values associated with chlorophyll content and to wavelengths ranges where (CR) methodology was applied; (iv) reflectance values of full spectrum. The best performances belonged to models using wider ranges of spectrum both in ODs and in SODs. The north cardinal point showed prediction models with the best performances in both varieties. No VI and CR indices showed reliable models. All the reliable prediction models were associated with compounds involved in physiological state and metabolism of leaves in apricot tree.

Introduction

Apricot (Prunus armeniaca L.) is an important fruit crop worldwide, belonging to Rosaceae family. The apricot production was estimated to be about 3,578,412.14 tonnes in the world and 695,632.70 tonnes in Europe, in 2021 (FAOSTAT, 2023). Europe is the second world’s apricot producer after Asia (Figure 1). Italy is the fourth world’s apricot producer, after Turkey, Iran and Uzbekistan, and it is the largest producer in Europe followed by France, Spain and Greece (Figure 1). In 2021, the apricot annual Italian production was 189 570 tonnes on 17 740 ha of cultivated area, in five Italian Regions: Emilia-Romagna (31%), Campania (21%), Basilicata (20%), Puglia (6%) and Sicilia (5%). In the Mediterranean area, a large part of production is primarily fresh fruit, with a smaller amount destined for processing industry for various products such as dried fruit, fruit cans, jam and juices, ice cream, cheese, etc. (Alvisi, 1997; Chang et al., 2016).

Figure 1. Production of apricot by continents (a) and countries (b) in the world (average 1994–2021).

It has been reported that mineral nutrition, particularly nitrogen content (N), plays a key role in plant growth, yield and fruit quality including, firmness, sugar content, phenolic compounds and visual appearance (skin colour) (García-Gomez et al., 2020; Zhebentyayeva et al., 2012; Radi et al., 1997, Radi et al., 2003; Falls and Siegel, 2005; Macheix et al., 2018; Wang et al., 2007; Asma et al., 2007; Dimitrovski and Cvetkovic, 1981). Since nitrogen content in plant was directly associated with chlorophyll synthesis (Boussadia et al., 2010; Kamnev et al., 2008), chlorophyll content in leaves is an indicator of the N status (Li et al., 2009; Shaahan et al., 1999). The proportion of leaf N allocated to the chloroplast is approximately 75% (Hak et al., 1993; Kutik et al., 1995). Leaf N is contained not only in chlorophyll but also in enzymes, vitamins and nucleic acids as well as in proteins. (Carranca et al., 2018; Khasawneh et al., 2021; Khasawneh et al., 2022; Mratinić et al., 2011).

Chlorophyll measurements performed by the SPAD (Soil and Plant Analysis Development) sensor (Minolta Corporation, Ltd., Osaka, Japan) (Minolta Camera, 1989) record chlorophyll values in a non-destructive way by acquiring values of the leaf transmittance at red (650 nm) and infrared (940 nm) wavelengths. The standard method for determining chlorophyll content is very accurate but destructive. (Amirruddin et al., 2020; Wu et al., 2023). Compared with the traditional destructive methods (Porra et al., 1989), SPAD analyses many leaf samples in small amounts of time, space and resources, leading to an exponential increase of its use in the last decade. (Uddling et al., 2007).

Hyperspectral analysis shares the versatility of SPAD, but in a full range of 350–2500 nm. It reads the reflectance with a higher resolution to gather more accurate information (Liu et al., 2020; Tang et al., 2022). These characteristics have been used to assess N content using vegetation indices such as Normalized Difference Nitrogen Index (NDNI) at specific wavelengths (Götze et al., 2010; Osborne et al., 2002) or greater ranges until to the full spectrum between 350–2500 nm (Bruning et al., 2019; Miao et al., 2009). To assess N content, Continuum Removal (CR) methodology was carried out (Kokaly and Clark, 1999) selecting the ranges where absorption peaks were evident along the spectrum (Curran et al., 2001; Huang et al., 2004; Van Der Meer, 2004). While SPAD directly measures chlorophyll at certain wavelengths and this value is correlated with nitrogen content, the spectroradiometer works on the range between 350 and 2500, detecting other signals in the short-wave infrared region (SWIR), between 1400 and 2500 nm, correlated with (a) nitrogen content not of chlorophyll but of other compounds as proteins, enzymes, involved in the plant metabolism; (b) other organic compounds as carbohydrates produced during chlorophyll photosynthesis. Thus, it might help to understand more fully the physiological state of the plant.

Leaf nutrient levels in apricots are non-uniform, showing seasonal variations (Leece and van den Ende, 1975) and dependence upon cultivar (Bojic et al., 1999), rootstock (Rosati et al., 1997, Velemis et al., 1999, Jiménez et al., 2004), interstock (Milosevic 2006, Milosevic and Milosevic 2011) and fertilization (Szücs 1986). It has been reported that the organization or relative amounts of photosynthetic components differ between sun and shade leaves (Hikosaka and Terashima, 1995; Hoel and Solhaug, 1998). Furthermore, an important component for a higher productivity of quality fruit in an orchard is the design (Javaid et al., 2017) for maximum light interception, and trees should be oriented in North-South direction (Javaid et al., 2017). Other authors (Boissard et al., 1990; Leinonen and Jones, 2004; Paltineanu et al., 2013; Zia et al., 2012, Wang et al., 2024) have studied how leaf orientation and canopy geometry represented by row orientation, row spacing and plant height interact with environmental factors, and the importance of cardinal point in peach, apple and walnut orchards, but not in apricots.

Based on these considerations, in this work an apricot orchard was studied with the following aims:

1. 1) to explore solutions that would make data collection more efficient considering how time-consuming using proximal sensing tools may be. We have studied whether the results of SPAD prediction models might vary depending on the leaf position on the tree. Following studies should evaluate whether some of these positions are more representative of the whole tree, allowing more targeted and efficient data collection. In this preliminary study, the four cardinal points were chosen because they represent easily identifiable standard positions on the apricot tree;

2. 2) to assess plant physiological state in an experimental study where synergy of SPAD and hyperspectral sensor was considered. This is a very preliminary attempt to assess how SPAD prediction models may describe metabolic mechanisms involving other substances, such as sugars, proteins, detected by hyperspectral analysis, during chlorophyll photosynthesis.

Prediction models of SPAD measurements were built using hyperspectral data as auxiliary variables along the entire spectrum between 350 and 2500 nm models or in ranges where absorption peaks were evident along the spectrum (Curran et al., 2001; Huang et al., 2004; Van Der Meer, 2004). These models were built on two types of datasets: (1) with all data collected on tree leaves for each of the two apricot varieties, Farlis and Farbaly; (2) with data grouped according to the four cardinal points on trees of the same apricot varieties.

Materials and methods

Description of site

The study was conducted on apricot orchard in the Apulia Region (Southern Italy) at latitude 40°53’26’’ N and longitude 17°5’5’’ in hilly territory and situated about 20 Km from the Adriatic Sea, in a private farm. The two varieties chosen were Farlis and Farbaly, both were about 8 years old, had a late ripening and a good fruit quality.

The orchard was grown in clay-loam soil and was irrigated using drip system, following the usual agricultural practices of the area. Orchard trees were spaced 3 m within rows and 5.5 m between rows. This study was carried out in July 2021.

Sampling design

Leaves of nine trees of Farlis and Farbaly were analysed in the field. Each tree was divided into 4 cardinal points east, north, south, west. Three leaves were selected on each cardinal point for a total of 108 leaves representing each variety. Three repetitions were measured in situ across the leaf with SPAD and after on the same points with Field Spec 4. Finally, the 324 SPAD and spectral data were averaged to get an overall dataset (OD) of 108 measurements that represented each variety. Furthermore, the data of each cardinal point of varieties were elaborated. For this aim, subsets of the overall dataset (SOD) of 27 SPAD and spectral data represented the cardinal points with each variety.

SPAD

The SPAD-502 (Minolta Corporation, Ltda., Osaka, Japan) (Minolta, 1989) measured the transmittance of red light (650 nm) and infrared radiation (940 nm) through the leaf giving out a SPAD dimensionless reading as an indicator of the amount of chlorophyll in the leaf tissue according to this equation (Naus et al., 2010) (Eq. 1):

(1) $$SPAD = kxlog\left( {{{\% trasmission\;940\;nm}}\over{{\% trasmission\;650\;nm}}} \right) + C$$

where k is a slope coefficient and C is a confidential offset value.

Spectral data

Hyperspectral analysis

Leaf spectral measurements were performed in the field with ASD Field Spec 4 Portable Spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado, USA) on the same points where SPAD analysis was performed. Plant Probe was used to detect a spectral signature in a range of 350–2500 nm. Field Spec 4 provided spectra with 2151 bands having a resolution of 1 nm. The spectral reflectance signatures were averaged over 10 nm to reduce the number of wavelengths from 2151 to 215, smoothing the spectra and keeping down the risk of over-fitting (Shepherd and Walsh, 2002). The calibration was performed by a standard white reference of Plant Probe with a known reflectance of 99% and repeated for each tree, to increase the comparability of measurements.

Spectral transformations

Since the aim of this paper was to study the performance of SPAD prediction models, vegetation indices (VI) associated with chlorophyll and nitrogen content were considered. They were Chlorophyll Index (CI), Normalized Pigment Chlorophyll Index (NPCI) associated with chlorophyll content and Normalized Difference Nitrogen Index (NDNI) with nitrogen content (Table 1).

Table 1. Vegetation indices ^*

* R_n represents the reflectance values and n the wavelength.

Furthermore, CR methodology was applied on all the ranges along the spectrum of 350–2500 nm showing absorbance peaks (R ranges) to compute D_epth (Table 2) as follows (Eq. 2):

(2) $${{\boldsymbol {D_{epth}}}}=1 - {{R_b}\over{{R_e}}}$$

where Rb is the reflectance at the band bottom and Re is the reflectance on the conjunction line called continuum at the same wavelength of Rb so that no local maximum is higher than 1 (Van Der Meer, 2004). This calculation was performed by ViewSpecPro software (Analytical Spectral Devices Inc., Boulder, Colorado, USA).

Table 2. Indices computed with continuum removal methodology ^*

* R indicate the ranges along the spectrum of 350–2500 nm showing absorbance peaks.

Statistical analysis

Prediction and model estimation

Two types of estimation models were applied on ODs and SODs constituted by SPAD values (dependent variable), and spectral data (independent variables or predictors): (i) ordinary least square regression (OLSR) applied on VI and CR indices; (ii) partial least square regression (PLSR) applied on full spectrum of 350–2500 nm (FS) and R ranges. The two apricot varieties were estimated separately, as well as the cardinal points. The analysis was performed with statistical software package SAS/STAT (release 9.4 SAS ANALYTICS U software).

Cross-validation

Cross-validation was performed as described by Riefolo et al. (2020). This procedure was performed on cardinal points subset too: two-third of the subset (18 samples) as calibration set, and the remaining one-third of the samples (9 samples), as validation set. The performance of prediction models was evaluated by means of three statistics: (i) the coefficient of determination in prediction (R²); (ii) root mean square error of prediction (RMSEP); (iii) residual prediction deviation (RPD) (Bellon-Maurel et al., 2010) defined as follows (Eq. 3):

(3) $${\bf{RPD}} = {\boldsymbol{SD/RMSEP}}$$

where SD is the standard deviation of the response variable SPAD. It is used to standardize the value of RMSEP with respect to the dispersion of samples enabling to compare the effectiveness of the prediction model as follows: (i) RPD > 2 excellent; (ii) 1.4 ≤ RPD ≤ 2 good; (iii) RPD < 1.4 unreliable (Chang et al., 2001). After cross-validation, analysis of residuals was performed with Shapiro–Wilk and Kolmogorov–Smirnov tests, to evaluate the normality of distribution. The selection of the best model was based on the following criteria: (i) RPD values ≥ 2.0 with the smaller number of latent variables; (ii) normality of residuals.

Analysis of variance

Analysis of variance of all variables relative to cardinal points was performed to find significative difference among them. The normal distribution of variables was verified by Shapiro-Wilk and Kolmogorov-Smirnov tests to choose the correct analysis of variance (data not shown). Test of Levene verified the homoscedasticity (data not shown). Based on these preliminary tests, four types of analysis of variance with their corresponding post hoc tests were applied (Table 3). Table 4 shows the results of some VI and CR indices of Farbaly, since SPAD, D_epth1460 and NPCI index did not show any significative differences.

Table 3. Types of test and post hoc test for analysis of variance

Table 4. Results of ANOVA for some variables concerning cardinal points in Farbaly ^*

* Different letters indicate significant differences (p < 0.05) among the cardinal points.

Results

A total of 23 SPAD prediction models were fitted for each variety: 8 models for wavelength ranges, full and partial (R ranges) elaborated with Partial Least Square Regression (PLSR) and 9 regarding VI and CR indices elaborated with Ordinary Least Square Regression (OLSR).

Analysis of normal distribution

Table 5 shows the basic statistics of the response variable SPAD for the two varieties in both ODs and SODs. Shapiro-Wilk and Kolmogorov-Smirnov tests were used to assess the normal distribution. When at least one of the two tests was significant at a level probability of 5% (p < 0.05), SPAD was transformed in Gaussian ranks by SAS/RANK procedure: the ranks divided by the total number of observations form values in the range 0–1, which were used in subsequent processing. Predictors (VI and CR indices and spectral data) and response variable (SPAD rank transformed) were centred and scaled to have the mean at zero and the variance at 1 and to place both on the same relative position to their variation in the process of prediction model estimation.

Table 5. Basic statistics of response variable SPAD for the two apricot varieties

* Mathematic factor computed by Shapiro-Wilk test.

† Mathematic factor computed by Kolmogorov-Smirnov test.

Prediction models

Farbaly OD

Table 6 shows PLSR statistics regarding R ranges. The only value of RPD ≥ 1.4 belonged to R1R6 range with 9 factors, (highest R² 0.623 and lowest RMSEP 0.610) (Figure 2). No VI and CR indices showed a RPD value ≥ 1.4 with an explained variance that never exceeded 10% (data not shown).

Table 6. Statistics of PLSR and analysis of residuals of SPAD for the two apricot varieties (OD)

* Mathematic factor computed by Shapiro-Wilk test.

† Mathematic factor computed by Kolmogorov-Smirnov test.

Figure 2. Predicted vs measured SPAD values in the R1R6 model for Farbaly (a) and in the FS model for Farlis (b).

Farbaly subsets

Table 7 shows PLSR statistics of SODs in descending order of RPD values until the last good effective model. Although the greatest value of RPD belonged to the R1R6 range in the east SOD (10.657), that represented by the R3 range of the same SOD was chosen as the best model (2.160) (Figure 3). It, among the models with an excellent RPD value, shows the lowest number of factors. Ten models have an excellent RPD value and a normal distribution of residuals (Table 7) and eight a good one (data not shown). Considering the models with RPD ≥ 1.4, the north SOD is represented six times while the west one only once (data not shown).

Table 7. The better models of the two apricot varieties concerning the cardinal points subsets (SOD) with analysis of residuals

In bold the models chosen for each variety.

* Mathematic factor computed by Shapiro-Wilk test.

† Mathematic factor computed by Kolmogorov-Smirnov test.

Figure 3. Predicted vs measured SPAD values in the R1R6 model (a) and in the R3 model for SOD east of Farbaly (b).

No VI and CR indices show a RPD value ≥ 1.4 both with an explained variance that never exceeds 50% (data not shown). The highest value of explained variance belongs to D_epth1000 (49,90%) with a RMSEP of 0,696 and a RPD of 1.386, followed by D_epth1460 (41,30%) with a RMSEP of 0,754 and a RPD of 1.280 in east SOD.

Farlis OD

Table 6 shows PLSR statistics regarding R ranges. The only value of RPD ≥ 2.0 belonged FS range, with 15 factors (highest R² 0.886 and lowest RMSEP 0.504) (Figure 2) while the other ranges never exceeded the 1.4 value. No VI and CR indices show a RPD value ≥ 1.4 both in OD with an explained variance that never exceeded 12% (data not shown).

Farlis subsets

Table 7 shows PLSR statistics of SODs in descending order of RPD values until the last good effective model. Although the greatest value of RPD belongs to the FS range in the north SOD (10.394), that represented by the R1 range of the east SOD was chosen as the best model (2.276), since it showed the lowest number of factors and a normal distribution of residuals (Figure 4). Nine models had an excellent RPD value and a normal distribution of residuals (Table 7), six a good one (data not shown). Since the west SOD model departed by the normal distribution, it is excluded by the effective models. Considering the models with RPD ≥ 1.4, the north SOD is represented six times, while the south one twice (data not shown).

No VI and CR indices show a RPD value ≥ 1.4 both with an explained variance that is around 15% (data not shown). The highest value of explained variance belongs to NDNI (15.44%) with a RMSEP of 1.470 and a RPD of 1.067, in east SOD, followed by NPCI (15.23%) with a RMSEP of 1.260 and a RPD of 1.062 in south SOD.

Figure 4. Predicted vs measured SPAD values in the R1 model for SOD east (a) and in the FS model for SOD north of Farlis (b).

Discussion

The models based on the wider ranges of spectrum, FS and R1R6, were the best ones both in Farlis and Farbaly ODs (Table 6). Considering the SODs results, the cardinal point east in Farbaly and the north one in Farlis showed the highest number of excellent models (Table 7). However, in Farbaly north SOD showed the highest number of models with RPD ≥ 1.4 (data not shown). These results were consistent with those of the ANOVA for Farbaly, since the north cardinal point showed the highest number of significative differences, 14, followed by the west one and as the lowest the south one (Table 4). Farlis showed a significative difference in CI index only between north and south cardinal point at a level of 1% (data not shown). The results suggested the possibility that metabolic behaviour could vary within the plant, even according to cardinal points and the importance of row direction in orchard design as reported by Javaid et al. (2017).

Considering the R ranges corresponding to the absorbance peaks where D_epths were calculated (Table 2), the R1 and R2 ranges included the wavelengths at which SPAD and the spectroradiometer both worked. From R3 onward, only the spectroradiometer was working. They have been associated with physiological characteristics of the plant based on previous studies. R1 (400–820 nm) corresponded to the absorbance range of chlorophyll while R2 (820–1110 nm) was associated with leaf structure (Bauer, 1985; Knipling, 1970; Peñuelas et al., 1993). The results showed excellent RPD for these two models to the north and east in both Farbaly and Farlis, and this was attributed to the fact that R1 and R2 ranges included SPAD wavelengths (650 and 930 nm). R3 range was associated with leaf water content (Clevers et al., 2010; González-Fernández et al., 2015). It became interesting to note that although there was no longer the overlap with the wavelengths where SPAD worked, for R3 the RPD value was still reliable in both Farbaly to the east (2.16) (Table 7) and Farlis to the north (1.88, not shown in Table 7). This could have been caused by the relationship between leaf structure, characterizing R2 where SPAD also worked, and R3 characterizing leaf water content. From the R3 range onward, the prediction models with the highest RPD no longer belonged to the same cardinal points in Farbaly and Farlis. Farbaly showed a good value of RPD for R6 model, whereas Farlis showed an excellent RPD value for R4 model. The ranges corresponding to R4 and R6, shared an association with organic compounds, involving stretching and bending deformations of O-H link as in the carbohydrates, over all in starch that represents plant energy reserve (Fourty et al., 1996). Therefore, these prediction models might be associated with photosynthetic activity. But whereas the R4 model would refer only to carbohydrates, the R6 one would also be associated with the presence of protein (Ecarnot et al., 2013; Fourty et al., 1996). Both varieties showed that the R4 and R6 models were mutually exclusive: when one model is reliable, the other is not. RPD value for R5 model associated with cellulose (Fourty et al., 1996; Shenk et al., 2001) forming the structural basis of the tree (roots, stems and leaves), resulted good only for Farbaly. This could confirm that the prediction models of SPAD produced are associated with compounds concerning the metabolic activity of leaf and not of tree structure. It is noteworthy that these compounds have a chemical affinity with water, associated with the ranges showing good predictive models at the north cardinal point. So, these results should confirm that the row direction in orchard design gets maximum light interception in trees as reported by Javaid et al. (2017).

Conclusion

In this paper, the variation of physiological response on different cardinal points of apricot trees was studied. This study is in addition to others with similar purposes carried out in the past on other types of orchards. The choice to assess the efficiency of SPAD prediction models on cardinal points showed the best results for the north one. VI and CR indices did not produce reliable predictive models even if their analysis of variance showed a greater number of significative differences for north. Considering both the OD and the subsets of cardinal points, models referring to the widest ranges of wavelengths showed the best performance. When the wavelengths ranges where SPAD worked no longer overlapped that of the hyperspectral sensor, the cardinal points of the best predictive models were not the same in the two varieties. These preliminary results, although agreeing with those of other studies, should be confirmed using more measurements taken on each cardinal point. They suggest the possibility of identifying plant points more suitable for producing reliable prediction models of SPAD that, involving many aspects of the leaf metabolic activity thanks to hyperspectral analysis, enable to assess plant physiological state.