Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing
Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a time...
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MDPI AG
2024-11-01
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| author | Guolan Xian Jiangang Liu Yongxin Lin Shuang Li Chunsong Bian |
| author_facet | Guolan Xian Jiangang Liu Yongxin Lin Shuang Li Chunsong Bian |
| author_sort | Guolan Xian |
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| description | Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R<sup>2</sup> 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period. |
| format | Article |
| id | doaj-art-dc417e9ebbac4f26b57e5bf32437502c |
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| issn | 2223-7747 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-dc417e9ebbac4f26b57e5bf32437502c2025-08-20T02:38:39ZengMDPI AGPlants2223-77472024-11-011323335610.3390/plants13233356Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote SensingGuolan Xian0Jiangang Liu1Yongxin Lin2Shuang Li3Chunsong Bian4State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaTimely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R<sup>2</sup> 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period.https://www.mdpi.com/2223-7747/13/23/3356UAVabove-ground biomassmulti-feature fusionmachine learningplant phenotypingpotato |
| spellingShingle | Guolan Xian Jiangang Liu Yongxin Lin Shuang Li Chunsong Bian Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing Plants UAV above-ground biomass multi-feature fusion machine learning plant phenotyping potato |
| title | Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing |
| title_full | Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing |
| title_fullStr | Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing |
| title_full_unstemmed | Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing |
| title_short | Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing |
| title_sort | multi feature fusion for estimating above ground biomass of potato by uav remote sensing |
| topic | UAV above-ground biomass multi-feature fusion machine learning plant phenotyping potato |
| url | https://www.mdpi.com/2223-7747/13/23/3356 |
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