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

2020 ◽  
Vol 12 (12) ◽  
pp. 2028 ◽  
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.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

scholarly journals Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes

2013 ◽  
Vol 16 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Daniel J. Karran ◽  
Efrat Morin ◽  
Jan Adamowski
Keyword(s):  
Wavelet Transforms ◽  
Model Performance ◽  
Probability Of Detection ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Non Linear ◽  
Using Data

Considering the popularity of using data-driven non-linear methods for forecasting streamflow, there has been no exploration of how well such models perform in climate regimes with differing hydrological characteristics, nor has the performance of these models, coupled with wavelet transforms, been compared for lead times of less than 1 month. This study compares the use of four different models, namely artificial neural networks (ANNs), support vector regression (SVR), wavelet-ANN, and wavelet-SVR in a Mediterranean, Oceanic, and Hemiboreal watershed. Model performance was tested for 1, 2 and 3 day forecasting lead times, measured by fractional standard error, the coefficient of determination, Nash–Sutcliffe model efficiency, multiplicative bias, probability of detection and false alarm rate. SVR based models performed best overall, but no one model outperformed the others in more than one watershed, suggesting that some models may be more suitable for certain types of data. Overall model performance varied greatly between climate regimes, suggesting that higher persistence and slower hydrological processes (i.e. snowmelt, glacial runoff, and subsurface flow) support reliable forecasting using daily and multi-day lead times.

scholarly journals Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization

2020 ◽  
Vol 12 (12) ◽  
pp. 1930 ◽  
Author(s):  
Hengqian Zhao ◽  
Chenghai Yang ◽  
Wei Guo ◽  
Lifu Zhang ◽  
Dongyan Zhang
Keyword(s):  
Disease Severity ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Hyperspectral Imagery ◽  
Vegetation Indices ◽  
The United States ◽  
Ground Survey ◽  
Severity Grading ◽  
Crop Disease ◽  
Survey Results

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

Applications of Data‐Driven Techniques in Filling Temporal Gaps Within and Between GRACE and GRACE-FO Records

2020 ◽  
Author(s):  
Mohamed Ahmed ◽  
Bimal Gyawali ◽  
David Wiese
Keyword(s):  
Vegetation Indices ◽  
Model Performance ◽  
Support Vector ◽  
Climate Indices ◽  
Efficiency Coefficient ◽  
Hydrologic Systems ◽  
Vector Machines ◽  
Terrestrial Water ◽  
Ann Models ◽  
Gravity Recovery

<p>Terrestrial water storage (TWS) data derived from past Gravity Recovery and Climate Experiment (GRACE; April 2002–June 2017) and current GRACE-Follow On (GRACE-FO; June 2018–present) missions provide insights into mass transport within, and between, different Earth’s systems (e.g., atmosphere, oceans, groundwater, and ice sheets). However, there are currently temporal gaps within GRACE-derived TWS record (20 months) and between GRACE and GRACE-FO missions (11 months), within GRACE-FO-derived TWS record (2 months), and similar gaps could be experienced between GRACE-FO and GRACE-II missions. In this study, we compare the performance of different data-driven techniques in filling TWS gaps for 62 global watersheds. Additionally, these techniques are being applied to reconstruct TWS globally on a grid scale (1° × 1°). We used artificial neural networks (ANNs), support vector machines (SVMs), and multiple linear regression (MLR) models to predict TWS data (04/2002 – 03/2020) based on the knowledge of relevant climatic datasets such as rainfall, temperature, evapotranspiration, vegetation indices, climate indices. The performance of the developed models was evaluated using several standard measures such as the root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliff efficiency coefficient (NSE). Our preliminary results indicate: (1) ANN models show higher performance over the examined watersheds compared to the other models (RMSE: 5.20; R: 0.93; NSE: 0.88), (2) the performances of ANN, MLR, and SVM models depend mainly on the nature of factors that control TWS in each of the examined hydrologic systems, and (3) higher model performance is achieved when the model input data were further spectrally decomposed. Results of our research could be used to validate GRACE-FO datasets. Our research will promote additional and improved use of GRACE products by the scientific community, end-users, and decision makers by providing a continuous uninterrupted TWS record from GRACE and GRACE-FO missions.</p>

Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine

2011 ◽  
Vol 55-57 ◽  
pp. 433-438
Author(s):  
Ya Zhao Zhang ◽  
Yao Xiang Li ◽  
Hong Fu Zhang ◽  
Hui Juan Zhang ◽  
Pai Li
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Building ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Infrared Spectrometry ◽  
Support Vector

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

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

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1436
Author(s):  
Johan Ninanya ◽  
David A. Ramírez ◽  
Javier Rinza ◽  
Cecilia Silva-Díaz ◽  
Marcelo Cervantes ◽  
...  
Keyword(s):  
Decision Support ◽  
Stomatal Conductance ◽  
Mean Squared Error ◽  
Satellite Image ◽  
Model Performance ◽  
Canopy Temperature ◽  
Coefficient Of Determination ◽  
Yield Prediction ◽  
Crop Models ◽  
Water Treatments

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.

Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

2020 ◽  
Vol 11 (3) ◽  
pp. 66-79 ◽  
Author(s):  
Miaomiao Ji ◽  
Keke Zhang ◽  
Qiufeng Wu
Keyword(s):  
Remote Sensing ◽  
Soil Temperature ◽  
Hybrid Model ◽  
Terrestrial Ecosystems ◽  
Hyperspectral Data ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Bp Model

Soil temperature, as one of the critical meteorological parameters, plays a key role in physical, chemical and biological processes in terrestrial ecosystems. Accurate estimation of dynamic soil temperature is crucial for underground soil ecological research. In this work, a hybrid model SAE-BP is proposed by combining stacked auto-encoders (SAE) and back propagation (BP) algorithm to estimate soil temperature using hyperspectral remote sensing data. Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.

Retrieval of Chlorophyll Content from Leaf Reflectance Spectra Using Support Vector Machine

2014 ◽  
Vol 602-605 ◽  
pp. 2313-2316
Author(s):  
Jie Lv ◽  
Zhen Guo Yan
Keyword(s):  
Support Vector Machine ◽  
Chlorophyll Content ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Support Vector ◽  
Transfer Model ◽  
Estimation Model ◽  
Suzhou City ◽  
Health Situation

The chlorophyll content in crop leaf is an indicator of health situation and the crop yield. Hence, it is very important to retrieval of accurate chlorophyll content in paddy rice. This research selected Zhengyi town of Suzhou city as the study area, measurements were acquired during the summer of 2009, in a field campaign in which for 288 rice leaf samples, rice hyperspectral data was measured by ASD FieldSpec3 spectrometer, chlorophyll content was measured by using a SPAD-502 chlorophyll meter. And the parameters of support vector machine were optimized by genetic algorithm, then support vector machine and PROSPECT radiative transfer model were adopted to build estimation model, which used to retrieve the chlorophyll content of rice. The results indicate that: the coefficient of determination for the rice chlorophyll estimation model is 0.8825, and RMSE is 8.7491. Research of this paper provides some reference for quickly and accurately estimating the chlorophyll content in rice.

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

AI ◽  
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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