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.

Highlights

  • The Leaf chlorophyll content (LCC) represents the photosynthetic rate, nitrogen content and health status of crops; it is an indicator of the nutrient status of crop plants and the degree of senescence [1]

  • The study was conducted during the 2016–2018 growing seasons in orchards located in Xinglin town, Fufeng County, Shaanxi Province, China (W 107◦ 450 –108◦ 030, N 34◦ 120 -34◦ 370 ) (Figure 1)

  • These results demonstrate that using spectral transformation (ST), vegetation indices (VIs) and three edge parameters (TEP) predicted LCC to have seasonable features

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Summary

Introduction

The Leaf chlorophyll content (LCC) represents the photosynthetic rate, nitrogen content and health status of crops; it is an indicator of the nutrient status of crop plants and the degree of senescence [1] It has significance for crop growth monitoring, nutrient content monitoring, quality evaluation and yield estimation [2]. Hyperspectral remote sensing (HRS) has become a major development trend in monitoring the chlorophyll content of vegetation due to its high spectral resolution, simplicity, effectiveness, and non-destructiveness [1,4,5] This method provides an effective means for research on the chlorophyll content of crops and the implementation of precision

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