Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards
The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement per...
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2024-11-01
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| author | Andrea Szabó János Tamás Attila Nagy |
| author_facet | Andrea Szabó János Tamás Attila Nagy |
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| description | The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R<sup>2</sup>) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHL<sub>apple1</sub> performed best (R<sup>2</sup> = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60, MBE = 84.59, and MAE = 243.39). |
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| spelling | doaj-art-fa9fea2deca44e16bbe9734241f5fb022024-12-27T14:29:05ZengMDPI AGHorticulturae2311-75242024-11-011012126610.3390/horticulturae10121266Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple OrchardsAndrea Szabó0János Tamás1Attila Nagy2Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Böszörményi út 138, 4032 Debrecen, HungaryInstitute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Böszörményi út 138, 4032 Debrecen, HungaryInstitute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Böszörményi út 138, 4032 Debrecen, HungaryThe main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R<sup>2</sup>) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHL<sub>apple1</sub> performed best (R<sup>2</sup> = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60, MBE = 84.59, and MAE = 243.39).https://www.mdpi.com/2311-7524/10/12/1266applechlorophyll estimator modelsvegetation index |
| spellingShingle | Andrea Szabó János Tamás Attila Nagy Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards Horticulturae apple chlorophyll estimator models vegetation index |
| title | Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards |
| title_full | Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards |
| title_fullStr | Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards |
| title_full_unstemmed | Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards |
| title_short | Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards |
| title_sort | spectral estimation of chlorophyll for non invasive assessment in apple orchards |
| topic | apple chlorophyll estimator models vegetation index |
| url | https://www.mdpi.com/2311-7524/10/12/1266 |
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