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| DOI | 10.3389/fpls.2024.1302435 |
| Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images | |
| 发表日期 | 2024 |
| ISSN | 1664-462X |
| 起始页码 | 15 |
| 卷号 | 15 |
| 英文摘要 | Introduction In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems.Methods Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression.Results and discussion The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R-2 = 0.81) and stomatal conductance (R-2 = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters. |
| 英文关键词 | remote sensing; physiology modeling; carob tree; machine learning; Random Forest regression |
| 语种 | 英语 |
| WOS研究方向 | Plant Sciences |
| WOS类目 | Plant Sciences |
| WOS记录号 | WOS:001194707900001 |
| 来源期刊 | FRONTIERS IN PLANT SCIENCE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/298799 |
| 作者单位 | Universita degli Studi di Bari Aldo Moro; Consejo Superior de Investigaciones Cientificas (CSIC); Universite de Gabes |
| 推荐引用方式 GB/T 7714 | . Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images[J],2024,15. |
| APA | (2024).Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.FRONTIERS IN PLANT SCIENCE,15. |
| MLA | "Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images".FRONTIERS IN PLANT SCIENCE 15(2024). |
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