Soil texture classification using multi class support vector machine

Barman, Utpal; Choudhury, Ridip Dev

doi:10.1016/j.inpa.2019.08.001

Cited by 99 publications

(50 citation statements)

References 20 publications

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“…SVMs have also previously been employed to predict soil SOC stock [84], phosphorus [85], and clay [85]. They were reported to be more suitable for classification than quantitative prediction [86]. Cubist has proven to be a feasible method for predicting soil properties, such as SOC [87], salinity [88], silt [89], and concentration of soil Cr, Pb, Cu, Zn, Ni, and As [90].…”

Section: Model Performance Employing the Different Methods And Elementsmentioning

confidence: 99%

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Xie

Zhu³

et al. 2021

Land

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Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

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Section: Model Performance Employing the Different Methods And Elementsmentioning

confidence: 99%

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Xie

Zhu³

et al. 2021

Land

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“…Alternatively, Barman et al used the support vector machine learning algorithm for soil texture classification. The classification of soil texture could be helpful when decide to cultivate the crops [9]. The predictive analysis was performed using the decision tree [10].…”

Section: Related Workmentioning

confidence: 99%

“…It is used in agriculture for machinery, crop, soil and livestock management [7]. The Support Vector Machine (SVM) algorithm has been used for soil texture classification and was found to be helpful in the choosing of crops [9]. Similarly, decision tree was used in performing the classification for predictive analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Waleed

Kamal

et al. 2021

Symmetry

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In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

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“…Soil texture affects the crop selection and regulates the water transmission property and is shown by the authors in (9) . Soil images are processed through the different stages, preprocessing of soil images for image enhancement, extracting the region of interest for segmentation and the texture analysis for feature vector.…”

Section: Background Studymentioning

confidence: 99%

Convolutional Neural Network approach for the prediction of Soil texture properties

Anandan¹

2021

IJST

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Background/Objectives: The main objective is to achieve improved performance of soil properties prediction for hyperspectral data. In this work, convolutional neural network is trained to understand the pattern of hyperspectral data by spatial interpolation. Methods/Statistical analysis: The proposed methodology is used to predict six soil properties-Organic Carbon content (OC), Cation Exchange Capacity (CEC), Nitrogen Content (N), pH level in water, Clay particle and Sand Particle. Soil texture which defines the relative content of soil particles is determined by the percentage of clay, sand and silt in the soil. The input to the Convolutional Neural Network (CNN) is the Hyperspectral data in the form of multiple arrays. The statistical evaluation of model performance is evaluated using root-mean-square error and r square. Findings: In this research, deep learning approach is used to capture the pattern hidden in the soil. Deep learning is a kind of neural network which can model complex relationship for representing non-linearity for a scalable data. The main challenge is predicting a soil type, as it involves complex structural characteristics and soil features. Novelty/Improvements: The performance of soil texture prediction is improved by automatic feature learning capability in the proposed CNN model. The average rmse value obtained in proposed method for all the six soil texture properties is 5.68%.

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Soil texture classification using multi class support vector machine

Cited by 99 publications

References 20 publications

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Convolutional Neural Network approach for the prediction of Soil texture properties

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