Rapid and Non-destructive Estimation of Apple Tree NPK Contents based on Leaf Spectral Analysis

Document Type : Research Article

Authors

• R. Azadnia ¹
• A. Rajabipour ²
• B. Jamshidi ³
• M. Omid ²

¹ PhD Student, Biosystems Engineering Department, Faculty of Agricultural Engineering and Technology, University of Tehran, Iran

² Biosystems Engineering Department, Faculty of Agricultural Engineering and Technology, University of Tehran, Iran

³ Smart Agricultural Research Department, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran

https://doi.org/10.22067/jam.2022.78770.1125

Abstract

Introduction
Apple is one of the most frequently consumed fruits in the world. It is a source of minerals, fiber, various biological compounds such as vitamin C, and phenolic compounds (natural antioxidants). The amount of nutrients plays a significant role in the growth, reproduction, and performance of agricultural products and plants. Chemical inputs can be accurately managed by predicting these elements. Thus, timely and accurate monitoring and managing the status of crop nutrition is crucial for adjusting fertilization, increasing the yield, and improving the quality. This approach minimizes the application of chemical fertilizers and reduces the risk of environmental degradation. In crop plants, leaf samples are typically analyzed to diagnose nutrient deficiencies and imbalances, as well as to evaluate the effectiveness of the current nutrient management system. Therefore, the main aim of this study is to estimate the level of Nitrogen (N), Phosphorus (P), and Potassium (K) elements in the leaves of the apple tree using the non-destructive method of Visible/Near-infrared (Vis/NIR) spectroscopy at the wavelength range of 500 to 1000 nm coupled with chemometrics analysis.
Materials and Methods
This research investigated the potential of the Vis/NIR spectroscopy coupled with chemometrics analysis for predicting NPK nutrient levels of apple trees. In this study, 80 leaf samples of apple trees were randomly picked and transferred to the laboratory for spectral measurement. The Green-Wave spectrometer (StellarNet Inc, Florida, USA) was utilized to collect the spectral data. In the next step, the spectral data were transferred to the laptop using the Spectra Wiz software (StellarNet Inc, Florida, USA). For this purpose, spectroscopy of the leaf samples was done in interactance mode. Ten random points were selected on each leaf to capture reflectance spectra and the averaged spectrum was used to determine the reflectance (R). The data was then transformed into absorbance (log 1/R) for chemometrics analysis. Following the spectroscopy measurements, the NPK contents were measured using reference methods. Afterward, Partial Least Square (PLS) multivariate calibration models were developed based on the reference measurements and spectral information using different pre-processing techniques. To remove the unwanted effects, various pre-processing methods were utilized to obtain an accurate calibration model. To evaluate the proposed models, the Root Mean Square Error of calibration and prediction sets (RMSEC and RMSEP), as well as the correlation coefficient of calibration and prediction sets (r_c and r_p), and Residual Predictive Deviation (RPD) were calculated.
Results and Discussion
The statistical metrics were calculated for the evaluation of PLS models and the results indicated that the PLS models could efficiently predict the NPK contents with satisfactory accuracy. The model with the best performance for nitrogen prediction was based on the standard normal variate pre-processing method in combination with the second derivative (SNV+D2) and resulted in r_c= 0.988, RMSEC=0.028%, r_p=0.978, RMSEP=0.034%, and RPD of 7.47. The best model for P content prediction resulted in r_c= 0.967, RMSEC=0.0051%, r_p=0.958, RMSEP=0.0057%, and RPD of 5.96. Additionally, the PLS model based on MSC+D2 pre-processing method resulted in r_c= 0.984, RMSEC=0.017%, r_p=0.976, RMSEP=0.021%, and RPD of 7.10, indicating the high potential of PLSR model in predicting K content. Moreover, the weakest performing model was related to the estimation of P content without pre-processing with r_c = 0.774, RMSEC = 0.013%, r_p = 0.685, RMSEP = 0.018%, and RPD value of 1.87. Based on the obtained results, the proposed PLS models coupled with suitable pre-processing methods were able to predict the nutrient content with high precision.
Conclusion
Field spectroscopy has recently gained popularity due to its portability, ease of use, and low cost. Consequently, the use of a portable system for estimating nutrient levels in the field can significantly save time and lower laboratory expenses. Therefore, due to the accuracy of the Vis/NIR spectroscopy technique and according to the obtained results, this method can be used to actualize a portable system based on Vis/NIR spectroscopy to estimate the nutrient elements needed by the apple trees in the orchards and to increase the productivity of the orchards.

Keywords

• Nutrients
• Partial Least Squares
• Pre-processing
• Spectroscopy
• Visible/Near-infrared

Main Subjects

• Precision Farming

©2022 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

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