Using PROSEPCT and SVM for the Estimation of Chlorophyll Concentration

This study focused on estimating chlorophyll concentration of rice using PROSPECT and support vector machine. The study site is located in West Lake sewage irrigation area of Changchun, Jiliin Province. Reflectance spectrual of rice were measured by ASD3 spectrometer, chlorophyll contents of rice were recorded with a portable chlorophyll meter SPAD-502. Support vector machines and PROSPECT model were adopted to construct hyperspectral models for predicting chlorophyll content. The results indicate that: the hyperspectral prediction model of rice chlorophyll content yields a maximum correlation coefficient of 0.8563, and achieves a smallest RMSE of 9.5106; and the prediction accuracy based on the first derivative spectrum is higher than on the original spectrum. Research of this paper provides a theoretical basis for large scale dynamic prediction of rice chlorophyll content in sewage irrigated area.

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Large-scale pinball twin support vector machines

Machine Learning ◽

10.1007/s10994-021-06061-z ◽

2021 ◽

Author(s):

M. Tanveer ◽

A. Tiwari ◽

R. Choudhary ◽

M. A. Ganaie

Keyword(s):

Support Vector Machines ◽

Large Scale ◽

Support Vector ◽

Twin Support Vector Machines ◽

Vector Machines

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A Sparse L 2-Regularized Support Vector Machines for Large-Scale Natural Language Learning

Information Retrieval Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-642-17187-1_33 ◽

2010 ◽

pp. 340-349

Author(s):

Yu-Chieh Wu ◽

Yue-Shi Lee ◽

Jie-Chi Yang ◽

Show-Jane Yen

Keyword(s):

Support Vector Machines ◽

Natural Language ◽

Language Learning ◽

Large Scale ◽

Support Vector ◽

Vector Machines ◽

Natural Language Learning

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Nutrients Assimilation and Chlorophyll Contents for Different Grapevine Varieties in Calcareous Soils in the Somontano DO (Spain)

Beverages ◽

10.3390/beverages4040090 ◽

2018 ◽

Vol 4 (4) ◽

pp. 90 ◽

Cited By ~ 3

Author(s):

José Casanova-Gascón ◽

Pablo Martín-Ramos ◽

Clara Martí-Dalmau ◽

David Badía-Villas

Keyword(s):

Chlorophyll Content ◽

Fruit Set ◽

Chlorophyll Concentration ◽

Calcareous Soils ◽

Pinot Noir ◽

Growth And Yield ◽

Cabernet Sauvignon ◽

Chlorophyll Contents ◽

Young Leaves ◽

The Impact

Lime-induced chlorosis (LIC) is an important abiotic constraint affecting the growth and yield of grapevines growing in calcareous soils in the Mediterranean region, and the sensory properties of the produced wine. In the work presented herein, the impact of LIC on the nutritional status and chlorophyll content was assessed for eleven varieties and a clone (Merlot, Pinot Noir, Cabernet Sauvignon, Tempranillo, Parraleta, Moristel, Aglianico, Macabeo, Sauvignon, Chardonnay, and Riesling), grafted to the same rootstock (1103 Paulsen). Macro- and micronutrient contents were determined in the fruit set and veraison stages by petiole analyses, while chlorophyll content in young leaves was monitored by SPAD. Significant differences were detected amongst varieties for all nutrients (including Fe), and inverse relationships between Fe and P contents in the petiole and chlorophyll concentration in the young leaves were found. Regarding LIC resistance, the Fe and chlorophyll contents suggest that Cabernet Sauvignon, Tempranillo and Aglianico varieties would show the best performance, while Sauvignon would be the least tolerant.

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An iterative method for classification of binary data

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa003 ◽

2020 ◽

Author(s):

Denali Molitor ◽

Deanna Needell

Keyword(s):

Binary Data ◽

Large Scale ◽

Support Vector ◽

Large Scale Data ◽

Classification Framework ◽

Vector Machines ◽

Inference Methods ◽

Compressed Data ◽

Scale Data

Abstract In today’s data-driven world, storing, processing and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis.

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