Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The origin...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/8/2075 |
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| author | Yu Zhang Qingrui Chang Yi Chen Yanfu Liu Danyao Jiang Zijuan Zhang |
| author_facet | Yu Zhang Qingrui Chang Yi Chen Yanfu Liu Danyao Jiang Zijuan Zhang |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R<sup>2</sup>), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry. |
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| language | English |
| last_indexed | 2024-03-11T00:12:10Z |
| publishDate | 2023-08-01 |
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| series | Agronomy |
| spelling | doaj.art-0f0ab5ee15854f6dae66a357b1150f9d2023-11-18T23:54:40ZengMDPI AGAgronomy2073-43952023-08-01138207510.3390/agronomy13082075Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost ModelYu Zhang0Qingrui Chang1Yi Chen2Yanfu Liu3Danyao Jiang4Zijuan Zhang5College of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Xianyang 712100, ChinaLeaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R<sup>2</sup>), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry.https://www.mdpi.com/2073-4395/13/8/2075hyperspectralleaf chlorophyll contentspectral transformationfeature band selectionCatBoost |
| spellingShingle | Yu Zhang Qingrui Chang Yi Chen Yanfu Liu Danyao Jiang Zijuan Zhang Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model Agronomy hyperspectral leaf chlorophyll content spectral transformation feature band selection CatBoost |
| title | Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model |
| title_full | Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model |
| title_fullStr | Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model |
| title_full_unstemmed | Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model |
| title_short | Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model |
| title_sort | hyperspectral estimation of chlorophyll content in apple tree leaf based on feature band selection and the catboost model |
| topic | hyperspectral leaf chlorophyll content spectral transformation feature band selection CatBoost |
| url | https://www.mdpi.com/2073-4395/13/8/2075 |
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