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.

scholarly journals Correction: Zhao, H., et al. Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization. Remote Sensing 2020, 12, 1930

2020 ◽  
Vol 12 (22) ◽  
pp. 3761
Author(s):  
Hengqian Zhao ◽  
Chenghai Yang ◽  
Wei Guo ◽  
Lifu Zhang ◽  
Dongyan Zhang
Keyword(s):  
Remote Sensing ◽  
Disease Severity ◽  
Vegetation Index ◽  
Automatic Estimation ◽  
Crop Disease ◽  
Severity Levels

The authors wish to make the following correction to this paper [...]

scholarly journals Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

2018 ◽  
Vol 10 (9) ◽  
pp. 1484 ◽  
Author(s):  
Liang Wan ◽  
Yijian Li ◽  
Haiyan Cen ◽  
Jiangpeng Zhu ◽  
Wenxin Yin ◽  
...  
Keyword(s):  
Image Classification ◽  
Oilseed Rape ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Multispectral Images ◽  
Flower Number ◽  
Coverage Area ◽  
Reference Images ◽  
Remote Estimation

Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape.

scholarly journals Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

2020 ◽  
Vol 36 (5) ◽  
Author(s):  
Marcela da Silva Flores ◽  
Willian Meniti Paschoalete ◽  
Fabio Henrique Rojo Baio ◽  
Cid Naudi Silva Campos ◽  
Ariane de Andréa Pantaleão ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Block Design ◽  
Vegetation Indices ◽  
Agronomic Performance ◽  
N Content ◽  
Maize Varieties ◽  
Leaf N Content ◽  
Leaf N

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

scholarly journals Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1842
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski ◽  
Michał Stępień ◽  
Stanisław Samborski ◽  
Dominik Ruciński ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Soil Nutrient ◽  
Growth Stages ◽  
Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

scholarly journals Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 226 ◽  
Author(s):  
Stefano Marino ◽  
Arturo Alvino
Keyword(s):  
High Resolution ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Aerial Vehicle ◽  
High Resolution Images

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

scholarly journals Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

2019 ◽  
Vol 9 (24) ◽  
pp. 5314 ◽  
Author(s):  
Marica Franzini ◽  
Giulia Ronchetti ◽  
Giovanna Sona ◽  
Vittorio Casella
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
P Value ◽  
Multispectral Imagery ◽  
Radiometric Calibration ◽  
Processing Strategies ◽  
Geometric Consistency ◽  
Sentinel 2

This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10−16).

Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

2020 ◽  
Author(s):  
Calogero Schillaci ◽  
Edoardo Tomasoni ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Northern Italy ◽  
Yield Data ◽  
Batch Mode ◽  
Area Index ◽  
Sentinel 2

&lt;p&gt;To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with &lt;25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R&lt;sup&gt;2&lt;/sup&gt;=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.&lt;/p&gt;

scholarly journals Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 457
Author(s):  
Rigas Giovos ◽  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Proximal Sensing ◽  
Spectral Bands ◽  
Sensing Platforms ◽  
Precision Viticulture ◽  
Remote Sensing Methods

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

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.

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