Artificial Neural Networks and Fuzzy Logic for Specifying the Color of an Image Using Munsell Soil-Color Charts

Pegalajar, M.C.; Sánchez‐Marañón, M.; Ruiz, L. G. B.; Mansilla, Luis; Delgado, Miguel

doi:10.1007/978-3-319-91473-2_59

Cited by 4 publications

(10 citation statements)

References 14 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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“…This chart, or the MSCB, is widely used by professional soil scientists for soil judging. The book consists of coloured papers or chips mounted on the hue cards, showing multiple variations of value and chroma in vertical and horizontal directions [ 10 ]. Users match the closest colour between their soil samples and the Munsell soil colour chips.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…The traditional method of soil colour determination involves mapping the human perception to the colour chips of the MSCB [ 6 ]. The process of using human perception to determine soil colours does not guarantee an accurate determination, as it differs from person to person and varying external conditions such as light and time of the day, as highlighted by [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of Munsell Soil Colour Using Smartphones

Nodi

Paul

Robinson

et al. 2023

Sensors

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Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.

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“…Soil color is one of the characteristics that can provide this information. Precisely assessing soil color is crucial since it yields vital data for soil scientists (Liu et al, 2020;Pegalajar et al, 2018Pegalajar et al, , 2020Pegalajar et al, , 2023. Soil color can provide valuable insights into soil development, composition, age of soil and rock surfaces, and variables that restrict plant growth.…”

Section: Introductionmentioning

confidence: 99%

Soil Color Comparison Using Munsell Soil Color Chart and Calibrated Smartphone Camera

Kautsar,

Faizah,

Uktoro

2024

Tekno. J. Ind. Tek.. Pert.

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Soil color is a crucial property in soil fertility assessment and monitoring. However, the subjective nature of the Munsell Soil Color Chart (MSCC) can lead to uncertainty in the analysis. To address this issue, a study was conducted to develop a soil color classification model from smartphone digital imagery based on color analysis and MSCC. The study involved taking 26 soil samples from various soil types and locations in the Special Region of Yogyakarta, Indonesia. Digital images of the soil were taken through a smartphone camera and compared with observations using MSCC to compare color differences (ΔE) based on Lab values. Soil images obtained from indoor studio conditions and calibration using spydercheckr in indoor and outdoor conditions are compared with MSCC and Chromameter values. The L*a*b color space was found to be superior to RGB for predicting and detecting small differences in color. The study also found that the Munsell soil color chart (MSCC) had a lower color difference than the chromameter in all lighting conditions, indicating that the MSCC or visual assessment can better detect the main soil color or soil matrix, while chromameter readings may have errors due to soil impurities.

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Artificial Neural Networks and Fuzzy Logic for Specifying the Color of an Image Using Munsell Soil-Color Charts

Cited by 4 publications

References 14 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

Determination of Munsell Soil Colour Using Smartphones

Soil Color Comparison Using Munsell Soil Color Chart and Calibrated Smartphone Camera

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