Use of ANN and visual-manual classification for prediction of soil properties for paving purposes

Souza, Wana Maria de; Ribeiro, Antonio Júnior Alves; Silva, Carlos Augusto Uchôa da

doi:10.1080/10298436.2020.1807546

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…To validate the proposed system introduced in this study, a comparison was accompanied with results presented in [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], where ANN and Convolutional Neural Network (CNN) was adopted to identify the soil types or soil properties as shown in Table 2 . The MSE/root MSE (RMSE), coefficient of determination, ANN inputs related to soil property such as Red, Blue, and Green colors, and ANN structure were considered for comparative purpose, where the values in these parameters were obtained from the computations in previous works and introduced in their results.…”

Section: Results Comparisonmentioning

confidence: 99%

“… Reference No. of inputs to ANN ANN structure MSE/RMSE (Training) MSE/RMSE (Testing) R 2 [ 43 ]/2018 (ANN) 3 (R, G, B) 3:1:60 1 × 10 −4 1 × 10 −4# 0.99 [ 44 ]/2017 (ANN) 8 (R, G, B, NIR, FC, NDVI, EVI, VHI) 8:14:10 0.05 ---- 0.94 [ 45 ]/2019 (ANN) 3 (R, G, B) 3:1:60 6.55 × 10 −4 5.25 × 10 −4# 0.99 [ 46 ]/2019 (CNN) 3 (R, G, B) Several layers --- 3.27∗ 0.96 [ 47 ]/2020 (CNN) 6 (soil property) Several layers --- 4.8∗ 0.86 [ 48 ]/2019 (CNN) Soil spectral data Several layers ---- 7.55∗ 0.7 [ 49 ]/2017 (ANN) 4 4:8:6:14 0.181∗ 0.163∗ 0.93 [ 50 ]/2020 (ANN) 5 (Color, Gravel, Sand, Silt Clay) 5:1:10 0.041 # 0.045 # 0.99 ANFIS (gebellmf) 3 (R, G, B) No. of mfs (3 3 3) 3.388 × 10 −3 3.378 × 10 −3 0.94 ANFIS (gebellmf) 3 (R, G, B) No.…”

Section: Results Comparisonmentioning

confidence: 99%

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Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Al-Naji

Fakhri

Gharghan

et al. 2021

Heliyon

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Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10 −6 (training), 1.004 × 10 −5 (testing) and 1.809 × 10 −5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.

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Section: Results Comparisonmentioning

confidence: 99%

Section: Results Comparisonmentioning

confidence: 99%

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Al-Naji

Fakhri

Gharghan

et al. 2021

Heliyon

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show abstract

“…Alawi and Rajab (2013) utilised multiple linear regression (MLR) analysis to predict the CBR of the unreinforced subbase soil layer. Recently, similar applications of advanced ML techniques for the prediction of CBR of different unreinforced soils were also conducted by other researchers (Erzin and Turkoz 2016, González Farias et al 2018, de Souza et al 2020, Nagaraju et al 2020, Tenpe and Patel 2020. Similarly, several studies were also carried out to evaluate permanent deformation and resilient modulus of recycled demolition wastes in pavements using ML algorithms (Arulrajah et al 2013, Ullah et al 2020, Ghorbani et al 2020a.…”

Section: Introductionmentioning

confidence: 93%

An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

Raja

Shukla

Khan

2021

International Journal of Pavement Engineering

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In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value.

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“…Guilherme (2016), baseado em projetos geotécnicos do Rio Grande do Norte realizou a predição da classificação AASHTO e do CBR a partir de variáveis biofísicas, geomorfométricas, de localização e de técnicas de modelagem como Regressão Múltipla e Redes Neurais Artificiais -RNA. Souza et al (2020), elaboraram um banco de dados de amostras de solos oriundos do Ceará e realizou a predição de modelos de Classificação AASHTO e CBR, utilizando a classificação tátil-visual como variáveis preditoras e RNA como técnica de modelagem.…”

Section: Fundamentação Teórica E Metodologiaunclassified

IV Seminário Socioambiental em Infraestrutura de Transportes VIA VIVA 2020

Brasil¹

2020

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Use of ANN and visual-manual classification for prediction of soil properties for paving purposes

Cited by 15 publications

References 18 publications

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study

An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

IV Seminário Socioambiental em Infraestrutura de Transportes VIA VIVA 2020

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