Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

Abstract

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

“…The reason may be that ML methods originated from training datasets that did not fully represent various natural variations, so their performance was inherently limited by the differences of environmental factors [63,64]. Figure 6 clearly showed that the estimation accuracy of LPPC had obvious seasonal variation, which was consistent with the results of Ta et al [11].…”

“…In recent decades, hyperspectral remote sensing has become a powerful tool for the monitoring of apple leaf photosynthetic pigment content (LPPC, a collective term for Cab and carotenoid content (Cxc) in this paper). For instance, Ta et al [11] used machine learning to enhance the estimation of apple leaf chlorophyll content from the original hyperspectral data. In addition, Cheng Li et al [12] developed a vegetation index-based support vector regression (SVR) method to retrieve apple tree canopy chlorophyll content from Sentinel-2A images.…”

“…Existing studies show that ML algorithms can accurately estimate the water content, nutritional status, chlorophyll content, and structural parameters of the vegetation [11,25,26]. By training variables and spectral features, the ML methods construct regression models for estimating biophysical parameters [27].…”

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

“…OSAVI (Optimized soil-adjusted vegetation index) [38] (1 + 0.16) (R800 − R670)/(R800 + R670 + 0.16) mSR 705 (Modified red edge simple ratio index) [42] (R750 − R445)/(R705 − R445) MTCI (MERIS terrestrial chlorophyll index) [35] (R754 − R709)/(R709 − R681) SIPI (Structure intensive pigment index) [25] (R800 − R445)/(R800 − R680) NPCI 680 (Normalized pigment chlorophyll index) [25] (R680 − R430)/(R680 + R430) NRI (Nitrogen reflectance index) [35] (R570 − R670)/(R570 + R670) NDRE (Normalized difference red-edge) [35] (R790 − R720)/(R790 + R720) DCNI (Double-peak canopy nitrogen index) [42] (R720 − R700)/(R700 − R670)/(R720 − R670 + 0.03) GNDVI (Green normalized difference vegetation index) [35] (R750 − R550)/(R750 + R550) MCARI2 (Modified triangular vegetation index 2) [35] 1.5(1.2(R800 − R550) − 2.5(R670 − R550))/ sqrt((2R800 + 1)2 − (6R800 − 5sqrt(R670)) − 0.5) CI red (Red-edge chlorophyll index) [43] R790/R720 − 1 CI green (Green chlorophyll index) [43] R790/R550 − 1 RVI 800 (Ratio vegetation index) [43] R800/R680 NDCI (Normalized difference chlorophyll index) [44] (R762 − R527)/(R762 + R527) GRVI (Green ratio vegetation index) [25] (R620 − R506)/(R620 + R506) TCARI (Transformed chlorophyll absorption in reflectance index) [38] 3 [(R700 − R670) − 0.2(R700 − R550)/(R700/R670)] NPCI 642 (Normalized pigment chlorophyll index) [25] (R642 − R432)/(R642 + R432) PPR (Plant pigment ratio) [25] (R503 − R436)/(R503 + R436) NDSI (Normalized difference spectral index) [25] (R813 − R763)/(R813 + R763) LCI (Leaf chlorophyll index) [25] (R850 − R710)/(R850 − R680) PRI (Photochemical reflectance index) [42] (R570 − R539)/(R570 + R539) VOG (Vogelman red edge index) [42] R740/R720 REP LI 780 (Red edge position: linear interpolation method) [42] 700 + 40 [(R670 + R780)/2 − R700]/(R740 − R700)…”

“…Recent studies showed that hyperspectral remote sensing technology has become a major development trend in monitoring the N content of crops due to its high spectral resolution, simplicity, effectiveness, and non-destructiveness [22][23][24]. Hyperspectral features, such as reflectance of sensitive band, "three edge" parameters, and vegetation indices (VIs) were used to identify sensitive regions to specific crop parameters [25]. Numerous studies showed the feasibility of using hyperspectral remote sensing for real-time monitoring of crop N nutrition status [26], such as leaf chlorophyll content (LCC) [27], LNC [28], leaf nitrogen accumulation (LNA) [29], plant nitrogen concentration (PNC) [30], plant nitrogen uptake (PNU) [31], nitrogen nutrition index (NNI) [32], etc.…”