利用支持向量机和相关向量机估算橡胶树的周长分布

IF 2.3 Q2 REMOTE SENSING
Bambang Hendro Trisasongko, Dyah Retno Panuju, Rizqi I’anatus Sholihah, Nur Etika Karyati
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引用次数: 0

摘要

在农业规划方面,空间数据取代了传统的表格数据,发挥了至关重要的作用。种植业是重要的农产品之一,由于其占地面积大,因此一直备受关注。空间机构提供了原始数据,通过机器学习的帮助,可以获得详细、最新的卫星数据来监测这一资源。本文讨论了使用支持向量机(SVM)和相关向量机(RVM)估算树围的机会,树围是树木成熟度和种植园生产力的预测指标。目前的研究表明,基线 SVR 模型无法产生足够的结果。问题的复杂性表明,只有径向基函数(RBF)核才有希望。在线性和多项式核上调整 SVM 并没有提高模型的质量,尽管似乎存在收益递减现象。经过参数调整后,该研究得出的模型均方根误差(RMSE)约为 8.5 厘米,R2 约为 0.69。虽然 RVM 是最近才引入的,但采用相同 RBF 核的 RVM 并没有产生足够的模型,RMSE 约为 52 厘米。由此得出结论,应通过研究多种机器学习方法来寻找最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the girth distribution of rubber trees using support and relevance vector machines

Within the context of agricultural planning, spatial data have played a crucial role, replacing conventional tabular-based data. Plantation, one of the key agricultural commodities, has been of interest since they occupy large coverage of landmass. Primary data supplies have been provided by space agencies, allowing detailed, updated satellite data to monitor this resource, with the aid of machine learning. This article discusses the opportunity of implementing support vector machines (SVM) and relevance vector machines (RVM) for estimating tree girth as a predictor of tree maturity and plantation productivity. The current research indicated that baseline SVR models were unable to yield a sufficient outcome. The complexity of the problem suggested that only the radial basis function (RBF) kernel was promising. Tuning SVM on linear and polynomial kernels did not enhance the quality of the models, although it appeared that the phenomenon of diminishing return existed. After parameter tuning, this research yielded a model with root mean squared error (RMSE) around 8.5 cm with R2 around 0.69. Although it was recently introduced, RVM with the same RBF kernel did not yield a sufficient model with RMSE about 52 cm. This concludes that the optimal model should be sought through examining a wide range of machine learning approaches.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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