Characterizing soil particle sizes using wavelet analysis of microscope images

Sudarsan, Bharath; Ji, Wenjun; Adamchuk, Viacheslav I.; Biswas, Asim

doi:10.1016/j.compag.2018.03.019

Cited by 25 publications

(4 citation statements)

References 26 publications

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“…This research is superior to this method because of its time advantage and the ability to use in-situ information to measure large-scale data sets. The RMSE of the optimal model in this study is 74.72, which is inferior to the results of Sudarsan et al [40] . Their study uses microscope images to study texture.…”

Section: Performance Comparison and Limitationcontrasting

confidence: 96%

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Meng

Yang

Ren

et al. 2023

International Journal of Agricultural and Biological Engineering

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Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R 2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis.

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Section: Performance Comparison and Limitationcontrasting

confidence: 96%

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Meng

Yang

Ren

et al. 2023

International Journal of Agricultural and Biological Engineering

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“…Ohm and Hryciw [16] developed a new image-based test called "sedimaging" to analyze particle sizes in the range of 0.075-2.0 mm, which has been typically performed by sieve analysis. Sudarsan et al [30] characterized soil particle sizes using image analysis of microscope images. Bittelli et al [31] conducted a comparative analysis of the pipette method, SediGraph method, LD method, and automated digital image analysis to determine the appropriate test method for particle size analysis of fine-graded soil and recommended the LD method as the standard method.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera

Kim,

Song,

Park

2023

Agriculture

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Soil particle size distribution is a crucial factor in determining soil properties and classifying soil types. Traditional methods, such as hydrometer tests, have limitations in terms of time required, labor, and operator dependency. In this paper, we propose a novel approach to quantify soil particle size analysis using machine vision analysis with an RGB camera. The method aims to overcome the limitations of traditional techniques by providing an efficient and automated analysis of fine-grained soils. It utilizes a digital camera to capture the settling properties of soil particles, eliminating the need for a hydrometer. Experimental results demonstrate the effectiveness of the machine vision-based approach in accurately determining soil particle size distribution. The comparison between the proposed method and traditional hydrometer tests reveals strong agreement, with an average deviation of only 2.3% in particle size measurements. This validates the reliability and accuracy of the machine vision-based approach. The proposed machine vision-based analysis offers a promising alternative to traditional techniques for assessing soil particle size distribution. The experimental results highlight its potential to revolutionize soil particle size analysis, providing precise, efficient, and cost-effective analysis for fine-grained soils.

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“…Sudarsan et al researched Microscope Image Processing (MIP) and developed a CWTbased computer vision algorithm to characterize soil particle size from digital images captured with a microscope. The wavelet technique's efficacy in detecting an image's particle size is promising, and the portability of the image acquisition device produces excellent proximate soil sensors (Sudarsan, Ji, Adamchuk, & Biswas, 2018). Santosh et al also research MIP.…”

Section: Introductionmentioning

confidence: 99%

Texture Feature Extraction of Pathogen Microscopic Image Using Discrete Wavelet Transform

Basri

2022

J. Ris. Inform.

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This study used a case study of Jabon leaves, and the pathogen is one of the causes of disease that attack the leaves of jabon, one of the leaf spots and leaf blight. Discovery of leaf spot disease in different pathogens and leaf blight. The pathogen was obtained from the leaf spot of Curvularia sp. 1 and Pestalotia sp., while the pathogen came from Curvularia sp. 2 and Botrytis sp. Identify the pathogen as soon as possible to minimize its effects. Improper handling can lead to increased virulence and resistance to the pathogen. Improper handling also can cause a disease outbreak (disease epidemic) in a region. This study is the first step in identifying the pathogens responsible for Jabon leaf disease. In this study, the Application of Koch's Postulates method to achieve the purification of pathogens and retrieve the microscopic pathogen image as the data acquisition stage. Furthermore, use of the segmentation stage to separate the object pathogen from the background, and one of the methods used is Otsu Thresholding. The extraction process of pathogen microscopic image using Discrete Wavelet Transform (DWT), DWT extraction results can be obtained using energy and entropy information.

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Characterizing soil particle sizes using wavelet analysis of microscope images

Cited by 25 publications

References 26 publications

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera

Texture Feature Extraction of Pathogen Microscopic Image Using Discrete Wavelet Transform

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