Canopy Temperature as a Key Physiological Trait to Improve Yield Prediction under Water Restrictions in Potato

Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R2) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes.

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Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽

10.3390/ma13051072 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1072 ◽

Cited By ~ 19

Author(s):

Dong Van Dao ◽

Hai-Bang Ly ◽

Huong-Lan Thi Vu ◽

Tien-Thinh Le ◽

Binh Thai Pham

Keyword(s):

Compressive Strength ◽

Civil Engineering ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Dry Density ◽

Statistical Measures ◽

Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

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Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

AI ◽

10.3390/ai1020015 ◽

2020 ◽

Vol 1 (2) ◽

pp. 229-241

Author(s):

Marcelo Chan Fu Wei ◽

Leonardo Felipe Maldaner ◽

Pedro Medeiros Netto Ottoni ◽

José Paulo Molin

Keyword(s):

Precision Agriculture ◽

Near Infrared ◽

Mean Squared Error ◽

Spectral Band ◽

Model Performance ◽

Important Predictor ◽

Predictor Variables ◽

Coefficient Of Determination ◽

Statistical Parameters ◽

Entire Dataset

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

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Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Water ◽

10.3390/w11071479 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1479 ◽

Cited By ~ 7

Author(s):

Liu ◽

Wang

Keyword(s):

Gene Expression ◽

Performance Evaluation ◽

Satellite Imagery ◽

Mean Squared Error ◽

Accuracy Assessment ◽

Gene Expression Programming ◽

Model Performance ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Model Performance Evaluation

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R2), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R2, RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation.

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Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning

Remote Sensing ◽

10.3390/rs12122028 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2028 ◽

Cited By ~ 3

Author(s):

Luwei Feng ◽

Zhou Zhang ◽

Yuchi Ma ◽

Qingyun Du ◽

Parker Williams ◽

...

Keyword(s):

Precision Agriculture ◽

Vegetation Indices ◽

Model Performance ◽

Original Data ◽

The United States ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Support Vector ◽

Yield Prediction ◽

Ensemble Model

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

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Models for wheat yield prediction using remotely sensed canopy temperature based indices

Journal of the Indian Society of Remote Sensing ◽

10.1007/bf03007236 ◽

1989 ◽

Vol 17 (4) ◽

pp. 9-18 ◽

Cited By ~ 1

Author(s):

S. K. Saha ◽

Ajai

Keyword(s):

Wheat Yield ◽

Canopy Temperature ◽

Remotely Sensed ◽

Yield Prediction

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How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

Epidemiology and Infection ◽

10.1017/s0950268821000091 ◽

2021 ◽

Vol 149 ◽

Author(s):

Junwen Tao ◽

Yue Ma ◽

Xuefei Zhuang ◽

Qiang Lv ◽

Yaqiong Liu ◽

...

Keyword(s):

Variable Selection ◽

Mean Squared Error ◽

Moving Average ◽

Dynamic Bayesian Networks ◽

Environmental Data ◽

Control Measures ◽

Coefficient Of Determination ◽

Percentage Error ◽

Analysis Strategy ◽

Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Sustainability ◽

10.3390/su13073727 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3727

Author(s):

Fatema Rahimi ◽

Abolghasem Sadeghi-Niaraki ◽

Mostafa Ghodousi ◽

Soo-Mi Choi

Keyword(s):

Regression Analysis ◽

Mean Squared Error ◽

Population Distribution ◽

Temporal Distribution ◽

Coefficient Of Determination ◽

Temporal Modeling ◽

Location Data ◽

Time Period ◽

Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

Scientific Reports ◽

10.1038/s41598-021-85371-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Daisuke Miyamori ◽

Takeshi Uemura ◽

Wenliang Zhu ◽

Kei Fujikawa ◽

Takaaki Nakaya ◽

...

Keyword(s):

Protein Folding ◽

Age Estimation ◽

Mean Squared Error ◽

Pearson Correlation ◽

Biological Clock ◽

Estimation Method ◽

Forensic Identification ◽

Random Coil ◽

Coefficient Of Determination ◽

Skin Samples

AbstractThe recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, RPF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and RPF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (RPF), squared (RPF2), sex, and RPF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and RPF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.

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