scholarly journals Investigating a Selection of Methods for the Prediction of Total Soluble Solids Among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data From Proximal and Remote Sensing

2021 ◽  
Vol 12 ◽  
Author(s):  
Aikaterini Kasimati ◽  
Borja Espejo-Garcia ◽  
Eleanna Vali ◽  
Ioannis Malounas ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Quality Characteristics ◽  
Growth Stages ◽  
Wine Grape ◽  
Remote Sensors ◽  
Grape Quality ◽  
Different Growth Stages

The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index (NDVI) data collected at different growth stages with non-destructive methods, such as proximal and remote sensing, that are currently used in precision viticulture (PV). The study involved several sets of high-resolution multispectral data derived from four sources, including two vehicle-mounted crop reflectance sensors, unmanned aerial vehicle (UAV)-acquired data, and Sentinel-2 (S2) archived imagery to estimate grapevine canopy properties at different growth stages. Several data pre-processing techniques were employed, including data quality assessment, data interpolation onto a 100-cell grid (10 × 20 m), and data normalization. By calculating Pearson’s correlation matrix between all variables, initial descriptive statistical analysis was carried out to investigate the relationships between NDVI data from all proximal and remote sensors and the grape quality characteristics in all growth stages. The transformed dataset was then ready and applied to statistical and machine learning algorithms, firstly trained on the data distribution available and then validated and tested, using linear and nonlinear regression models, including ordinary least square (OLS), Theil–Sen, and the Huber regression models and Ensemble Methods based on Decision Trees. Proximal sensors performed better in wine grapes quality parameters prediction in the early season, while remote sensors during later growth stages. The strongest correlations with the sugar content were observed for NDVI data collected with the UAV, Spectrosense+GPS (SS), and the CropCircle (CC), during Berries pea-sized and the Veraison stage, mid-late season with full canopy growth, for both years. UAV and SS data proved to be more accurate in predicting the sugars out of all wine grape quality characteristics, especially during a mid-late season with full canopy growth, in Berries pea-sized and the Veraison growth stages. The best-fitted regressions presented a maximum coefficient of determination (R2) of 0.61.

scholarly journals Factors Involved on Tiger-Stripe Foliar Symptom Expression of Esca of Grapevine

Plants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1041
Author(s):  
Francesco Calzarano ◽  
Giancarlo Pagnani ◽  
Michele Pisante ◽  
Mirella Bellocci ◽  
Giuseppe Cillo ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Central Italy ◽  
Growth Stages ◽  
Symptom Expression ◽  
Strong Response ◽  
Water Index ◽  
High Calcium ◽  
Calcium Magnesium ◽  
Different Growth Stages

Esca of grapevine causes yield losses correlated with incidence and severity symptom expression. Factors associated with leaf symptom mechanisms are yet to be fully clarified. Therefore, in 2019 and 2020, macro and microelement analyses and leaf reflectance measurements were carried out on leaves at different growth stages in a vineyard located in Abruzzo, central Italy. Surveys were carried out on leaves of both never leaf-symptomatic vines and different categories of diseased vine shoots. Never leaf-symptomatic and diseased vines were also treated with a fertilizer mixture that proved to be able to limit the symptom expression. Results showed that untreated asymptomatic diseased vines had high calcium contents for most of the vegetative season. On the contrary, treated asymptomatic diseased vines showed higher contents of calcium, magnesium, and sodium, at berries pea-sized, before the onset of symptoms. These vines had better physiological efficiency showing higher water index (WI), normalized difference vegetation index (NDVI), and green normalized difference vegetation index (GNDVI) values, compared to untreated asymptomatic vines, at fruit set. Results confirmed the strong response of the plant to symptom expression development and the possibility of limiting this response with calcium and magnesium applications carried out before the symptom onset.

scholarly journals Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

2021 ◽  
Vol 14 (1) ◽  
pp. 120
Author(s):  
Razieh Barzin ◽  
Hossein Lotfi ◽  
Jac J. Varco ◽  
Ganesh C. Bora
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Vegetation Index ◽  
Growth Stages ◽  
Fertilizer N ◽  
Red Edge ◽  
Leaf N Concentration ◽  
Different Growth Stages ◽  
N Status ◽  
Leaf N

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

scholarly journals Remote Estimation of Biomass in Winter Oilseed Rape (Brassica napus L.) Using Canopy Hyperspectral Data at Different Growth Stages

2019 ◽  
Vol 9 (3) ◽  
pp. 545 ◽  
Author(s):  
Yi Ma ◽  
Shenghui Fang ◽  
Yi Peng ◽  
Yan Gong ◽  
Dong Wang
Keyword(s):  
Oilseed Rape ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Quantitative Relation ◽  
Growth Stages ◽  
Winter Oilseed Rape ◽  
Correlation Analyses ◽  
Different Growth Stages

The dry aboveground biomass (AGB) is an important parameter in assessing crop growth and predicting yield. This study aims to ascertain the optimal methods for the spectroscopic estimation of winter oilseed rape (WOR) biomass. The different fertilizer-N gradients WOR were planted to collect biomass data and canopy hyperspectral data in two years of field experiments. Correlation analyses and partial least squares regression (PLSR) were performed between canopy hyperspectral data and AGB, and the linear and non-linear regression models simulated the quantitative relation between the vegetation indices (VIs) and AGB at four different growth stages (seeding, bolting, flowering, and pod stage). The results indicated that VIs that were derived from canopy hyperspectral data could estimate AGB accurately: (1) At the seeding and bolting stage, the CIred edge showed excellent performance with the higher accuracy (R2 ranged from 0.60–0.95) as compared to the other six VIs (Green chlorophyll index (CIgreen), normalized difference vegetation index (NDVI), Green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), DVI, and soil adjusted vegetation index (SAVI)); (2) Correlation analyses and PLSR can effectively extract the feature wavelengths (800 nm and 1200 nm) for biomass estimation. The modified vegetation indices NDVI (800, 1200) significantly improved AGB estimation accuracy (R2 > 0.80, RMSE < 1530 kg/hm2, RPD > 2.3) without saturation phenomenon at the total for four stages, and retained good robustness and reduced the influence of flower and pod for estimating AGB; (3) it was vital to pay more attention to the near-infrared (NIR) bands that could represent WOR growth phenology, and selecting suitable VIs and modeling algorithms could also have a relatively large effect on the success of AGB estimation. The overall results indicated that WOR AGB could be reliably estimated by canopy hyperspectral data, although the plant architecture and coverage of WOR were significantly different during its entire growing period.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

&lt;p&gt;The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level.&amp;#160;&lt;/p&gt;&lt;p&gt;Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Assessing water demand with remote sensing for two coriander varieties

2018 ◽  
Vol 36 (3) ◽  
pp. 266-273
Author(s):  
Euseppe Ortiz ◽  
Enrique A. Torres
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Demand ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Camera ◽  
Crop Coefficient ◽  
Remote Sensors ◽  
The Relationship ◽  
Important Basis

The use of remote sensing to determine water needs has been successfully applied by several authors to different crops, maintaining, as an important basis, the relationship between the normalized difference vegetation index (NDVI) and biophysical variables, such as the fraction of coverage (fc) and the basal crop coefficient (Kcb). Therefore, this study quantified the water needs of two varieties of coriander (UNAPAL Laurena CL and UNAPAL Precoso CP) based on the response of fc and Kcb, using remote sensors and a water balance according to the FAO-56 methodology. A Campbell Scientific meteorological station, a commercial digital camera and a portable spectro radiometer were used to obtain information on the environmental conditions and the crop. By means of remote sensing associated with a water balance, it was found that the water demand was 156 mm for CL and 151 mm for CP until the foliage harvest (41 d after sowing); additionally, the initial Kcb was 0.14, the mean Kcb was 1.16 (approximately) and the final Kcb was 0.71 (approximately).

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

scholarly journals The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4937 ◽  
Author(s):  
Ziqing Xia ◽  
Yiping Peng ◽  
Shanshan Liu ◽  
Zhenhua Liu ◽  
Guangxing Wang ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Southern China ◽  
Regional Scale ◽  
Growth Stages ◽  
Maturity Stage ◽  
Cultivated Land ◽  
Flowering Stage ◽  
Area Index ◽  
Land Quality

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

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