scholarly journals On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dorée and Grapevine Trunk Diseases

2018 ◽  
Vol 11 (1) ◽  
pp. 23 ◽  
Author(s):  
Johanna Albetis ◽  
Anne Jacquin ◽  
Michel Goulard ◽  
Hervé Poilvé ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Roc Curve ◽  
Vegetation Indices ◽  
Carotenoid Content ◽  
Multispectral Imagery ◽  
Biophysical Parameters ◽  
Wine Production ◽  
Flavescence Dorée ◽  
Grapevine Trunk Diseases ◽  
Spectral Bands ◽  
Trunk Diseases

Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).

scholarly journals Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

2021 ◽  
Vol 3 (1) ◽  
pp. 29-49
Author(s):  
Ghizlane Astaoui ◽  
Jamal Eddine Dadaiss ◽  
Imane Sebari ◽  
Samir Benmansour ◽  
Ettarid Mohamed
Keyword(s):  
Random Forest ◽  
Nitrogen Uptake ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Wheat Crop ◽  
Multispectral Imagery ◽  
Biophysical Parameters ◽  
Phenological Stages ◽  
Wheat Varieties

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha.

scholarly journals Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2618
Author(s):  
Łukasz Jełowicki ◽  
Konrad Sosnowicz ◽  
Wojciech Ostrowski ◽  
Katarzyna Osińska-Skotak ◽  
Krzysztof Bakuła
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Frost Damage ◽  
Crop Damage ◽  
Multispectral Imagery ◽  
Spectral Bands ◽  
Red Edge ◽  
Object Based ◽  
Direct Measurements

This research is related to the exploitation of multispectral imagery from an unmanned aerial vehicle (UAV) in the assessment of damage to rapeseed after winter. Such damage is one of a few cases for which reimbursement may be claimed in agricultural insurance. Since direct measurements are difficult in such a case, mainly because of large, unreachable areas, it is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage. In this experiment, UAV images were taken using a Sequoia multispectral camera that collected data in four spectral bands: green, red, red-edge, and near-infrared. Data were acquired from three altitudes above the ground, which resulted in different ground sampling distances. Within several tests, various vegetation indices, calculated based on four spectral bands, were used in the experiment (normalized difference vegetation index (NDVI), normalized difference vegetation index—red edge (NDVI_RE), optimized soil adjusted vegetation index (OSAVI), optimized soil adjusted vegetation index—red edge (OSAVI_RE), soil adjusted vegetation index (SAVI), soil adjusted vegetation index—red edge (SAVI_RE)). As a result, selected vegetation indices were provided to classify the areas which qualified for reimbursement due to frost damage. The negative influence of visible technical roads was proved and eliminated using OBIA (object-based image analysis) to select and remove roads from classified images selected for classification. Detection of damaged areas was performed using three different approaches, one object-based and two pixel-based. Different ground sampling distances and different vegetation indices were tested within the experiment, which demonstrated the possibility of using the modern low-altitude photogrammetry of a UAV platform with a multispectral sensor in applications related to agriculture. Within the tests performed, it was shown that detection using UAV-based multispectral data can be a successful alternative for direct measurements in a field to estimate the area of winterkill damage. The best results were achieved in the study of damage detection using OSAVI and NDVI and images with ground sampling distance (GSD) = 10 cm, with an overall classification accuracy of 95% and a F1-score value of 0.87. Other results of approaches with different flight settings and vegetation indices were also promising.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals Production of Phytotoxic Metabolites by Botryosphaeriaceae in Naturally Infected and Artificially Inoculated Grapevines

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 802
Author(s):  
Pierluigi Reveglia ◽  
Regina Billones-Baaijens ◽  
Jennifer Millera Millera Niem ◽  
Marco Masi ◽  
Alessio Cimmino ◽  
...  
Keyword(s):  
Fungal Pathogens ◽  
Protocatechuic Acid ◽  
Physiological Factors ◽  
Diplodia Seriata ◽  
Grapevine Trunk Diseases ◽  
Botryosphaeria Dieback ◽  
Trunk Diseases ◽  
One Year ◽  
Pathogen Dna

Grapevine trunk diseases (GTDs) are considered a serious problem to viticulture worldwide. Several GTD fungal pathogens produce phytotoxic metabolites (PMs) that were hypothesized to migrate to the foliage where they cause distinct symptoms. The role of PMs in the expression of Botryosphaeria dieback (BD) symptoms in naturally infected and artificially inoculated wood using molecular and analytical chemistry techniques was investigated. Wood samples from field vines naturally infected with BD and one-year-old vines inoculated with Diplodia seriata, Spencermartinsia viticola and Dothiorella vidmadera were analysed by cultural isolations, quantitative PCR (qPCR) and targeted LC-MS/MS to detect three PMs: (R)-mellein, protocatechuic acid and spencertoxin. (R)-mellein was detected in symptomatic naturally infected wood and vines artificially inoculated with D. seriata but was absent in all non-symptomatic wood. The amount of (R)-mellein detected was correlated with the amount of pathogen DNA detected by qPCR. Protocatechuic acid and spencertoxin were absent in all inoculated wood samples. (R)-mellein may be produced by the pathogen during infection to break down the wood, however it was not translocated into other parts of the vine. The foliar symptoms previously reported in vineyards may be due to a combination of PMs produced and climatic and physiological factors that require further investigation.

scholarly journals Assessment of Conjugate Complexes of Chitosan and Urtica dioica or Equisetum arvense Extracts for the Control of Grapevine Trunk Pathogens

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 976
Author(s):  
Natalia Langa-Lomba ◽  
Laura Buzón-Durán ◽  
Pablo Martín-Ramos ◽  
José Casanova-Gascón ◽  
Jesús Martín-Gil ◽  
...  
Keyword(s):  
Urtica Dioica ◽  
Gas Chromatography Mass Spectrometry ◽  
Equisetum Arvense ◽  
Grapevine Trunk Diseases ◽  
Trunk Diseases ◽  
Synergistic Behavior ◽  
Reported Data ◽  
Basic Substances

In the work presented herein, we analyze the efficacy of three basic substances that comply with European Regulation (EC) No 1107/2009, namely chitosan, horsetail (Equisetum arvense L.) and nettle (Urtica dioica L.), for the control of grapevine trunk diseases (GTDs) in organic farming. The E. arvense and U. dioica aqueous extracts, prepared according to SANCO/12386/2013 and SANTE/11809/2016, have been studied by gas chromatography–mass spectrometry (GC-MS), identifying their main active constituents. The three basic substances, either alone or in combination (forming conjugate complexes), have been tested in vitro against eight Botryosphaeriaceae species, and in vivo, in grafted plants artificially inoculated with Neofusicoccum parvum and Diplodia seriata. A clear synergistic behavior between chitosan and the two plant extracts has been observed in the mycelial growth inhibition tests (resulting in EC90 values as low as 208 μg·mL−1 for some of the isolates), and statistically significant differences have been found in terms of vascular necroses lengths between treated and non-treated plants, providing further evidence of aforementioned synergism in the case of D. seriata. The reported data supports the possibility of extending the applications of these three basic substances in Viticulture beyond the treatment of mildew.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

The compositional turnover of grapevine-associated plant pathogenic fungal communities are greater among intraindividual microhabitats and terroirs than among healthy and Esca-diseased plants

2021 ◽  
Author(s):  
Adrienn Geiger ◽  
Zoltán Karácsony ◽  
Richárd Golen ◽  
Kálmán Zoltán Váczy ◽  
József Geml
Keyword(s):  
Pathogenic Fungi ◽  
Wine Industry ◽  
Fungal Communities ◽  
Opportunistic Pathogens ◽  
Grapevine Trunk Diseases ◽  
Esca Disease ◽  
Dna Metabarcoding ◽  
Trunk Diseases ◽  
Fungal Dna ◽  
Key Questions

Grapevine trunk diseases (GTD) are a major threat to the wine industry, causing yield loss and dieback of grapevines. While the increasing damage caused by GTDs in recent decades have spurred several studies on grapevine-associated pathogenic fungi, key questions about the emergence and severity of GTDs remain unanswered, including possible differences in plant pathogenic fungal communities in asymptomatic and symptomatic grapevines. We generated fungal DNA metabarcoding data from soil, bark, and perennial wood samples from asymptomatic and symptomatic grapevines sampled in three terroirs. We observed larger compositional differences in plant pathogenic fungi among different plants parts within grapevine plants than among individual grapevines. This is driven by the dominance of GTD-associated fungi in perennial wood and non-GTD pathogens in soil, as well as by the lack of significant differences among asymptomatic and Esca symptomatic grapevines. These results suggest that fungi generally associated with Esca disease belong to the core grapevine microbiome and likely are commensal endophytes and/or latent saprotrophs, some of which can act as opportunistic pathogens on stressed plants. In addition, we found significant compositional differences among sampling sites, particularly in soil, which suggest a certain influence of local edaphic and mesclimatic factors on plant pathogenic fungal communities. Furthermore, the observed differences among terroirs in plant pathogenic fungal communities in grapevine woody parts indicate that environmental factors likely are important for the development of Esca disease and further studies are needed to investigate the abiotic conditions on fungal compositional dynamics in Esca-affected plants.

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