Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

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Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes

Chemosensors ◽

10.3390/chemosensors9030055 ◽

2021 ◽

Vol 9 (3) ◽

pp. 55

Author(s):

Salah Elsayed ◽

Salah El-Hendawy ◽

Mosaad Khadr ◽

Osama Elsherbiny ◽

Nasser Al-Suhaibani ◽

...

Keyword(s):

Fuzzy Inference System ◽

Spectral Reflectance ◽

Fuzzy Inference ◽

Plant Traits ◽

Irrigation Management ◽

Inference System ◽

Irrigation Regimes ◽

Neuro Fuzzy ◽

Reflectance Indices ◽

Spectral Reflectance Indices

Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions.

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Use of Jacobians for inverse kinematics of articulated robots : a study on approximate solutions

10.32469/10355/86541 ◽

2020 ◽

Author(s):

◽

Uriel Jacket Tresor Demby's

Keyword(s):

Numerical Solution ◽

Inverse Kinematics ◽

Degrees Of Freedom ◽

Numerical Solutions ◽

Fuzzy Inference ◽

Numerical Approach ◽

Data Driven ◽

Approximate Methods ◽

Inference System ◽

First Case

In the context of articulated robotic manipulators, the Forward Kinematics (FK) is a highly non-linear function that maps joint configurations of the robot to poses of its endeffector. Furthermore, while in the most useful cases these functions are neither injective (one-to-one) nor surjective (onto), depending on the robot configuration -- i.e. the sequence of prismatic versus revolute joints, and the number of Degrees of Freedom (DoF) -- the associated Inverse Kinematics (IK) problem may be practically or even theoretically impossible to be solved analytically. Therefore, in the past decades, several approximate methods have been developed for many instances of IK problems. The approximate methods can be divided into two distinct categories: data-driven and numerical approaches. In the first case, data-driven approaches have been successfully used for small workspace domains (e.g., task-driven applications), but not fully explored for large ones, i.e. in task-independent applications where a more general IK is required. Similarly, and despite many successful implementations over the years, numerical solutions may fail if an improper matrix inverse is employed (e.g., Moore-Penrose generalized inverse). In this research, we propose a systematic, robust and accurate numerical solution for the IK problem using the Unit-Consistent (UC) and the Mixed (MX) Inverse methods to invert the Jacobians derived from the Denavit-Hartenberg (D-H) representation of the FK for any robot. As we demonstrate, this approach is robust to whether the system is underdetermined (less than 6 DoF) or overdetermined (more than 6 DoF). We compare the proposed numerical solution to data driven solutions using different robots -- with DoF varying from 3 to 7. We conclude that numerical solutions are easier to implement, faster, and more accurate than most data-driven approaches in the literature, specially for large workspaces as in task-independent applications. We particularly compared the proposed numerical approach against two data-driven approaches: Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while exploring various architectures of these Neural Networks (NN): i.e. number of inputs, number of outputs, depth, and number of nodes in the hidden layers.

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Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions

Sensors ◽

10.3390/s20226569 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6569 ◽

Cited By ~ 1

Author(s):

Adel H. Elmetwalli ◽

Salah El-Hendawy ◽

Nasser Al-Suhaibani ◽

Majed Alotaibi ◽

Muhammad Usman Tahir ◽

...

Keyword(s):

Vegetative Growth ◽

Growth Stage ◽

Plant Traits ◽

Growth Traits ◽

Water Status ◽

Accurate Estimation ◽

Growth Stages ◽

Irrigation Level ◽

Arid Conditions ◽

Irrigation Regimes

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.

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Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

Journal of Water and Climate Change ◽

10.2166/wcc.2018.191 ◽

2018 ◽

Vol 11 (1) ◽

pp. 200-216 ◽

Cited By ~ 2

Author(s):

Reza Haji Hosseini ◽

Saeed Golian ◽

Jafar Yazdi

Keyword(s):

Climate Change ◽

General Circulation ◽

Climate Models ◽

Fuzzy Inference ◽

Global Climate ◽

General Circulation Models ◽

Global Climate Models ◽

Data Driven ◽

Observation Data ◽

Inference System

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

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Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models

Water ◽

10.3390/w13101383 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1383

Author(s):

Eunjeong Lee ◽

Taegeun Kim

Keyword(s):

Water Quality ◽

River Basin ◽

Fuzzy Inference ◽

Oxygen Demand ◽

Data Driven ◽

Anfis Model ◽

Hydrological Simulation ◽

Inference System ◽

Hydrological Conditions ◽

Irrigation Period

The water quality of the Dongjin River deteriorates during the irrigation period because the supply of river maintenance water to the main river is cut off by the mass intake of agricultural weirs located in the midstream regions. A physics-based model and a data-driven model were used to predict the water quality in the Dongjin River under various hydrological conditions. The Hydrological Simulation Program–Fortran (HSPF), which is a physics-based model, was constructed to simulate the biological oxygen demand (BOD) in the Dongjin River Basin. A Gamma Test was used to derive the optimal combinations of the observed variables, including external water inflow, water intake, rainfall, and flow rate, for irrigation and non-irrigation periods. A data-driven adaptive neuro-fuzzy inference system (ANFIS) model was then built using these results. The ANFIS model built in this study was capable of predicting the BOD from the observed hydrological data in the irrigation and non-irrigation periods, without running the physics-based model. The predicted results have high confidence levels when compared with the observed data. Thus, the proposed method can be used for the reliable and rapid prediction of water quality using only monitoring data as input.

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Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

Annals of Biomedical Engineering ◽

10.1007/s10439-020-02628-4 ◽

2020 ◽

Vol 48 (12) ◽

pp. 2976-2987

Author(s):

Abdul Haleem Butt ◽

Erika Rovini ◽

Hamido Fujita ◽

Carlo Maremmani ◽

Filippo Cavallo

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Fuzzy Inference ◽

Inertial Sensors ◽

Objective Assessment ◽

Data Driven ◽

Inference System ◽

Regression Algorithms ◽

Severity Levels ◽

Wearable Inertial Sensors

AbstractParkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

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Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system

Environmental Modelling & Software ◽

10.1016/j.envsoft.2017.11.026 ◽

2018 ◽

Vol 106 ◽

pp. 35-47 ◽

Cited By ~ 18

Author(s):

N. Bartoletti ◽

F. Casagli ◽

S. Marsili-Libelli ◽

A. Nardi ◽

L. Palandri

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Data Driven ◽

Rainfall Runoff ◽

Inference System ◽

Neuro Fuzzy

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An Improved Data-Driven Model for the Prediction of Minimum Transport Condition for Sand Transport in Multiphase Flow Systems

FUOYE Journal of Engineering and Technology ◽

10.46792/fuoyejet.v6i1.618 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Adegboyega B Ehinmowo ◽

Oluwadamilare O Ariyo ◽

Oluwatosin A Ohiro ◽

Olawale T Fajemidupe ◽

Kazeem K Salam

Keyword(s):

Multiphase Flow ◽

Response Surface ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Data Driven ◽

Sand Transport ◽

Predictive Capability ◽

Inference System ◽

Condition Optimization ◽

Transport Condition

The correct prediction of minimum transport condition (MTC) is of great importance to the oil and gas industry. The sand deposition is an associated problem of multiphase transportation of oil, gas and or solid. The purpose of this work is to investigate the predictive capability of three different data-driven approaches: Artificial neural networks (ANN), Adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) and response surface methodologies (RSM). The models were developed using182 experimental data points with input parameters such as liquid superficial velocity, pipe diameter, particle size, pipe inclination and the output parameter predicted is the minimum transport condition (velocity) for sand particles. The developed models were compared with existing models. The results showed that the three methods performed creditably well in the prediction of MTC with ANFIS having the highest predictive capability with an R2 value of 0.99997 and an average error value of 0.00035836 compared with ANN and RSM having R2 value of 0.9998 and 0.9973 respectively. The three data-driven techniques investigated in this study also outperformed published correlations for the prediction of MTC. The findings from this research can be invaluable for the effective and robust management of sand transport in multiphase flow systems.Keywords— Artificial Intelligence, Fuzzy Inference System, Model, Minimum Transport Condition, Optimization methods, Response Surface Methodology

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Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a9628.1111121 ◽

2021 ◽

Vol 11 (1) ◽

pp. 70-80

Author(s):

Hela Lassoued ◽

◽

Raouf Ketata ◽

Hajer Ben Mahmoud ◽

◽

...

Keyword(s):

Fuzzy Inference System ◽

Time Scale ◽

Gradient Descent ◽

Fuzzy Inference ◽

Data Driven ◽

Comparison Study ◽

Inference System ◽

Input Feature ◽

Neuro Fuzzy ◽

Driven System

This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive ( SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done.

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Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Water ◽

10.3390/w13192666 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2666

Author(s):

Salah Elsayed ◽

Salah El-Hendawy ◽

Yaser Hassan Dewir ◽

Urs Schmidhalter ◽

Hazem H. Ibrahim ◽

...

Keyword(s):

Grain Yield ◽

Water Content ◽

Multivariate Regression ◽

Regression Models ◽

Plant Traits ◽

Water Status ◽

Growth Conditions ◽

Leaf Water Status ◽

Potential Yield ◽

Irrigation Regimes

Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

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