APPLICATION OF SOFT COMPUTING METHODS AND SPECTRAL REFLECTANCE DATA FOR WHEAT GROWTH MONITORING

Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artiﬁcial Neural Networks (ANNs), as a soft computing methods, are one of the most efﬁcient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.

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Spectral signature of brown rust and orange rust in sugarcane

Revista Facultad de Ingeniería Universidad de Antioquia ◽

10.17533/udea.redin.20191042 ◽

2020 ◽

pp. 9-20

Author(s):

Eniel Rodríguez-Machado ◽

Osmany Aday-Díaz ◽

Luis Hernández-Santana ◽

Jorge Luís Soca-Muñoz ◽

Rubén Orozco-Morales

Keyword(s):

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Brown Rust ◽

Spectral Signature ◽

Spatial And Temporal Variability ◽

Mean Values ◽

Index Value

Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.

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Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran

Journal of Water Supply Research and Technology—AQUA ◽

10.2166/aqua.2021.150 ◽

2021 ◽

Author(s):

Alireza Emadi ◽

Sarvin Zamanzad-Ghavidel ◽

Reza Sobhani ◽

Ali Rashid-Niaghi

Keyword(s):

Soft Computing ◽

Mean Squared Error ◽

Gene Expression Programming ◽

Groundwater Resources ◽

Computing Methods ◽

Climatic Conditions ◽

Inference System ◽

Deep Well ◽

Deep Wells ◽

Soft Computing Methods

Abstract In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm−1 in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins.

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Estimation of current-induced pile groups scour using a rule-based method

Journal of Hydroinformatics ◽

10.2166/hydro.2012.175 ◽

2012 ◽

Vol 15 (2) ◽

pp. 516-528 ◽

Cited By ~ 16

Author(s):

N. Ghaemi ◽

A. Etemad-Shahidi ◽

B. Ataie-Ashtiani

Keyword(s):

Soft Computing ◽

Fuzzy Inference ◽

Computing Methods ◽

Accurate Estimation ◽

Scour Depth ◽

Pile Groups ◽

Empirical Formulas ◽

Inference System ◽

Acceptable Risks ◽

Soft Computing Methods

Scour phenomenon around piles could endanger the stability of the structures placed on them. Therefore, an accurate estimation of the scour depth around piles is very important for engineers. Due to the complexity of the interaction between the current, seabed and pile group; prediction of the scour depth is a difficult task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the prediction of the scour depth. However, these methods do not give enough insight into the generated models and are not as easy to use as the empirical formulas. In this study, new formulas are given that are compact, accurate and physically sound. In comparison with the other soft computing methods, this approach is more transparent and robust. Comparison between the developed formulas and previous empirical formulas showed the superiority of the developed ones in terms of accuracy. In addition, the given formulas can be easily used by engineers to estimate the scour depth around pile groups. Moreover, in this study, design factors are given for different levels of acceptable risks, which can be useful for design purposes.

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Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Remote Sensing ◽

10.3390/rs13040579 ◽

2021 ◽

Vol 13 (4) ◽

pp. 579

Author(s):

Xueqin Jiang ◽

Shenghui Fang ◽

Xia Huang ◽

Yanghua Liu ◽

Linlin Guo

Keyword(s):

Time Series ◽

Precision Agriculture ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Southern China ◽

Growth Period ◽

Growth Monitoring ◽

Rice Growth ◽

Red Edge

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

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A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters

Knowledge-Based Engineering and Sciences ◽

10.51526/kbes.2021.2.1.31-46 ◽

2021 ◽

Vol 2 (1) ◽

pp. 31-46 ◽

Cited By ~ 1

Author(s):

Rifat Tur ◽

Serbay Yontem

Keyword(s):

Soft Computing ◽

Wave Height ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Computing Methods ◽

Coastal Structures ◽

Inference System ◽

Neuro Fuzzy ◽

Black Sea Coast ◽

Soft Computing Methods

In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Network (MLPNN) and Adaptive-Neuro Fuzzy Inference System (ANFIS) models were developed to predict wave height parameters. Various input combinations were selected to create seven different sub-models. These sub-models were applied using developed MLPNN and ANFIS models. Accuracy of sub-models were evaluated for each wave height parameters in terms of performance evaluation criteria. The results showed that the wave height parameters predicted by the MLPNN and ANFIS methods are similar and both methods yield results acceptable for design purposes. However, for maximum wave height, Hmax, ANFIS sub-model yields slightly better results.

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Comparison of Spectral Reflectance and Multispectrally Induced Fluorescence to Determine Winter Wheat Nitrogen Deficit

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1059.127 ◽

2014 ◽

Vol 1059 ◽

pp. 127-133 ◽

Cited By ~ 1

Author(s):

Jana Galambošová ◽

Miroslav Macák ◽

Marek Živčák ◽

Vladimír Rataj ◽

Pavol Slamka ◽

...

Keyword(s):

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

N Uptake ◽

Health Effect ◽

Growth Stages ◽

Commercial Technology ◽

Reflectance Indices ◽

Crop Health

Technical and technological aspects of variable rate nitrogen fertilization receive much attention nowadays. Current commercial technology is based on the use of spectral reflectance of crop. However, these have some limitations as variety dependence, crop health effect and limited use in more developed growth stages. New parameters overcoming these problems need to be assessed and their potential in precision agriculture should be considered. Multispectrally induced fluorescence is a progressive method. In addition to chlorophyll content, it allows to determine phenolic compounds, which is a product of metabolism of the plant under nitrogen deficit and is considered as the most exact indicator of nitrogen deficit. Comparing the spectral reflectance indices (normalized difference vegetation index – NDVI and normalized difference red edge index – NDRE) and multispectral fluorescence index (nitrogen balance index – NBI), these performed similarly in terms of determining the leaves biomass and nitrogen content in %, NDRE and NBI reflected significantly also aboveground N; however, only the correlation of NDVI reflected with N uptake and with leaf area was highly significant.

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