Deciphering nitrogen concentrations in Metasequoia glyptostroboides: a novel approach using RGB images and machine learning

IF 3.4 2区 农林科学 Q1 FORESTRY
Cong Ma, Ran Tong, Nianfu Zhu, Wenwen Yuan, Yanji Li, G. Geoff Wang, Tonggui Wu
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引用次数: 0

Abstract

Recent advances in spectral sensing techniques and machine learning (ML) methods have enabled the estimation of plant physiochemical traits. Nitrogen (N) is a primary limiting factor for terrestrial forest growth, but traditional methods for N determination are labor-intensive, time-consuming, and destructive. In this study, we present a rapid, non-destructive method to predict leaf N concentration (LNC) in Metasequoia glyptostroboides plantations under N and phosphorus (P) fertilization using ML techniques and unmanned aerial vehicle (UAV)- based RGB (red, green, blue) images. Nine spectral vegetation indices (VIs) were extracted from the RGB images. The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine, random forest (RF), and multiple linear regression, gradient boosting regression and classification and regression trees (CART). The results show that RF is the best fitting model for estimating LNC with a coefficient of determination (R2) of 0.73. Using this model, we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P. Height, diameter at breast height (DBH), and crown width of all M. glyptostroboides were analyzed by Pearson correlation with the predicted LNC. DBH was significantly correlated with LNC under N treatment. Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient, scalable, and cost-effective method for LNC quantification. Future research can extend this approach to different tree species and different plant traits, paving the way for large-scale, time-efficient plant growth monitoring.

Abstract Image

水杉氮浓度解密:一种利用 RGB 图像和机器学习的新方法
光谱传感技术和机器学习(ML)方法的最新进展使得对植物理化性状的估计成为可能。氮(N)是陆地森林生长的主要限制因素,但传统的氮测定方法耗费大量人力、时间,而且具有破坏性。在本研究中,我们提出了一种快速、非破坏性的方法,利用 ML 技术和基于无人机(UAV)的 RGB(红、绿、蓝)图像来预测水杉在氮磷施肥条件下的叶片氮浓度(LNC)。从 RGB 图像中提取了九种光谱植被指数(VIs)。利用光谱反射率和植被指数作为输入特征,构建了基于支持向量机、随机森林(RF)、多元线性回归、梯度提升回归和分类回归树(CART)的 LNC 估算模型。结果表明,RF 是估计 LNC 的最佳拟合模型,决定系数 (R2) 为 0.73。利用该模型,我们评估了氮和磷处理对 LNC 的影响,发现氮会显著增加,而磷会显著减少。我们通过皮尔逊相关性分析了所有甘蓝小檗的高度、胸径(DBH)和冠幅与预测 LNC 的关系。在氮处理下,DBH 与 LNC 有明显相关性。我们的研究结果凸显了将无人机 RGB 图像与 ML 算法相结合,作为一种高效、可扩展且经济有效的 LNC 定量方法的潜力。未来的研究可以将这种方法扩展到不同的树种和不同的植物性状,为大规模、省时的植物生长监测铺平道路。
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来源期刊
CiteScore
7.30
自引率
3.30%
发文量
2538
期刊介绍: The Journal of Forestry Research (JFR), founded in 1990, is a peer-reviewed quarterly journal in English. JFR has rapidly emerged as an international journal published by Northeast Forestry University and Ecological Society of China in collaboration with Springer Verlag. The journal publishes scientific articles related to forestry for a broad range of international scientists, forest managers and practitioners.The scope of the journal covers the following five thematic categories and 20 subjects: Basic Science of Forestry, Forest biometrics, Forest soils, Forest hydrology, Tree physiology, Forest biomass, carbon, and bioenergy, Forest biotechnology and molecular biology, Forest Ecology, Forest ecology, Forest ecological services, Restoration ecology, Forest adaptation to climate change, Wildlife ecology and management, Silviculture and Forest Management, Forest genetics and tree breeding, Silviculture, Forest RS, GIS, and modeling, Forest management, Forest Protection, Forest entomology and pathology, Forest fire, Forest resources conservation, Forest health monitoring and assessment, Wood Science and Technology, Wood Science and Technology.
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