The use of Vis-NIR-SWIR spectroscopy in the prediction of soil available ions after application of rock powder

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2024-03-01 DOI:10.1016/j.inpa.2022.07.001
Marlon Rodrigues , Josiane Carla Argenta , Everson Cezar , Glaucio Leboso Alemparte Abrantes dos Santos , Önder Özal , Amanda Silveira Reis , Marcos Rafael Nanni
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Abstract

Some of the problems attributed to traditional laboratory analyses that limit the correct assessment of the nutrient contents in the soil are time requirements and high cost of the soil nutrient determinations. To solve these problems, a study was carried out to evaluate the use of visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR) spectroscopy in the prediction of soil available ions submitted to the application of rock powders. The study was carried out on an Arenosol in Paranavaí City/Brazil. Treatments (rock powders) were arranged within a split-plot system designed in randomized blocks with four repetitions. Sugarcane was cultivated for 14 months after the application of rock powders. Later, 96 soil samples were collected for measuring the pH and available ions P, K+, Ca2+, Mg2+, S-SO42-, Si, Cu2+, Fe2+, Mn2+, and Zn2+ as well as spectral reading through a Vis-NIR-SWIR spectroradiometer to predict the soil chemical attributes through the partial least square regression (PLS) technique. The results showed that the elements K+, Ca2+, Mg2+, Cu2+, and Fe2+ could be predicted with a reasonable rightness degree (R2p > 0.50, RPDp > 1.40) from spectral models. However, for the attributes pH, P, S-SO42-, Si, Mn2+, and Zn2+, there were no satisfactory models (R2p < 0.50, RPDp < 1.40). Thus, the application of rock powder changed the spectral curves and, because of that, allows the building of PLS models to predict the elements K+, Ca2+, Mg2+, Cu2+, and Fe2+. Therefore, Vis-NIR-SWIR spectroscopy is a promising alternative to the routine analyses of soil fertility since it has advantages such as fast analytical speed, low cost, easy to operate, non-destructive, and environmentally friendly, because it does not use harmful chemicals.

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应用Vis-NIR-SWIR光谱法预测岩石粉施用后土壤有效离子
传统的实验室分析方法存在一些问题,限制了对土壤中养分含量的正确评估,其中包括时间要求和土壤养分测定的高成本。为了解决这些问题,我们开展了一项研究,评估使用可见光、近红外和短波红外(Vis-NIR-SWIR)光谱预测施用岩粉后土壤中可用离子的情况。这项研究是在巴西帕拉纳瓦市的阿雷诺索尔进行的。各处理(岩粉)被安排在随机区组设计的四次重复的分块系统中。施用石粉后,甘蔗种植了 14 个月。随后,采集了 96 个土壤样本,测量 pH 值和可利用离子 P、K+、Ca2+、Mg2+、S-SO42-、Si、Cu2+、Fe2+、Mn2+ 和 Zn2+,并通过可见光-近红外-西红外光谱仪读取光谱,通过偏最小二乘法回归(PLS)技术预测土壤化学属性。结果表明,光谱模型对 K+、Ca2+、Mg2+、Cu2+ 和 Fe2+ 等元素的预测具有合理的正确度(R2p > 0.50,RPDp > 1.40)。然而,对于 pH、P、S-SO42-、Si、Mn2+ 和 Zn2+,没有令人满意的模型(R2p < 0.50,RPDp < 1.40)。因此,岩石粉末的应用改变了光谱曲线,因此可以建立 PLS 模型来预测 K+、Ca2+、Mg2+、Cu2+ 和 Fe2+ 等元素。因此,可见光-近红外-西红外光谱仪具有分析速度快、成本低、操作简便、无损、不使用有害化学物质等优点,是土壤肥力常规分析的一种有前途的替代方法。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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