Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem grapevine/Armillaria

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticultur

Implementação de modelos de espectroscopia hiperespectral e nanossatélite na identificação de cultivares de vitis vinifera e suas variações regionais

O Brasil é destaque na produção mundial de uvas e demonstra uma constante evolução ao longo de sua histórica, desde 1980, com o Estado do Rio Grande do Sul, no topo da lista de produtores. Diversas regiões produtoras de uvas e vinhos no Brasil tem organizado suas atividades no sentido de se tornarem reconhecidas como “Indicações de Procedência” (IP), dando tipicidade e caráter regional aos seus produtos. Esta caracterização requer descrições dos impactos das condições ambientais e do trabalho humano. A utilização de dados adquiridos por sensoriamento remoto, incluindo dados proximais hiperespectrais e de satélites, permitem classificar e caracterizar as variedades de uvas e suas respectivas unidades produtoras de diversas localidades, sob condições climáticas e antrópicas diferenciadas. Esta tese tem como principal objetivo desenvolver uma metodologia para aquisição de dados, treinamento de modelos de hiperespectrais por sensor proximal e imagens via nanossatélite. A área de estudo é composta por oito vinhedos comerciais localizados no Rio Grande do Sul, Brasil. Na primeira fase deste estudo, a unidade de análise foi a folha isolada da videira em diferentes regiões. Posteriormente foi realizado o levantamento dos parâmetros de clorofila, Teor de Sólidos Totais (TST) ou °Brix da uva, espectros de reflectância hiperespectral e imagens de nanossatélite em parcelas de Cabernet Sauvignon em uma vinícola da Serra Gaúcha. Os modelos Light Gradient Booster Machine (LGBM) e Random Forest (RF) obtiveram as melhores acurácias na discriminação espectral em regiões do ultravioleta (UV) e visível (VIS). As estimativas apresentaram elevados R² com o modelo de regressão RF. O índice de Gini teve maiores valores para comprimentos de onda no UV/VIS/NIR e o índice de vegetação Plant Senescence Reflectance Index (PSRI) teve melhor desempenho para predição dos parâmetros de clorofila, e o Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) para o ºBrix da uva, utilizando como dados a reflectância hiperespectral e a reflectância de superfície. Desenvolvimentos futuros incluem o levantamento de dados com maior número de planta e variedades, auxiliando a compreender as assinaturas espectrais de cada variedade como subsídio para um melhor manejo da produção.Brazil has had an increasing prominence in the production of grapes in the world and the country's production history since the 80's demonstrates this constant evolution. At the top of the list of producers is the State of Rio Grande do Sul. Several grape and wine producing regions in Brazil have organized their activities in order to become recognized as “Indications of Origin” (IO), giving their products typicality and regional character. This characterization requires descriptions of environmental conditions and the impacts of these conditions and human work. The use of remote sensing data, including proximal hyperspectral and satellite data, allow us to classify and characterize grape varieties and their respective producing units from various locations, under different climatic and anthropic conditions. The main objective of this thesis is to develop a methodology for data acquisition, training of plant spectroscopy models with a hyperspectral proximal sensor and for nanosatellite imaging. . The study area consists of eight commercial vineyards found in Rio Grande do Sul, Brazil. In the first phase of this study, the unit of analysis was the leaf isolated from the vine in different regions. Subsequently, a survey of chlorophyll parameters, Total Solids Content (°Bx) of the grape, hyperspectral reflectance spectra and nanosatellite images were conducted in Cabernet Sauvignon plots in a Serra Gaúcha winery. Machine learning algorithms were applied in the discrimination of vineyards by region and by variety, and in the estimation of the chlorophyll and Brix parameters of the grape. The Light Gradient Booster Machine (LGBM) and Random Forest (RF) models obtained the best accuracies in spectral discrimination in the ultraviolet (UV) and visible (VIS) regions. The estimates showed high R² with the RF regression model. The Gini index had higher values for UV/VIS/NIR wavelengths, and the Plant Senescence Reflectance Index (PSRI) had better performance for predicting chlorophyll parameters, and the Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) for the degree Brix, using as data the hyperspectral reflectance and the surface reflectance. Future developments include collecting data with a greater number of plants and varieties, helping to understand the spectral signatures of each variety as a subsidy for better production management

Poster Presentations

THEME I - A Worldwide Perspective on Viticultural ZoningTHEME II - Methodological Approach to ZoningTHEME: III - Practical Application of ZoningTHEME IV - Viticulture, Landscapes and the Marketing of our WineTHEME VI - Role of Climate/Soil of Different Zones/Terroirs on Grape CharacteristicsTHEME VII - Role of Trellising System and Canopy Size/Exposure in Zone/Terroir Expr~ssionIBEME VID -The Role of Soil Water Holding Capacity and Plant Water Relations in Zone/Terroir ExpressionTI:IEl\ilE IX - Role ~f Harvesting Time/Optimal Ripeness (Evolution of. Ripening) in Zone/Terroir ExpressionTHEME XII~ Tools for Optimizing Grape and Wine Quality     

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Utilização de técnicas de radiometria e geotecnologias na descrição do comportamento espectral de cultivares de vitis vinífera : estudo de caso na serra gaúcha

A importância da Indicação Geográfica (IG), o cenário econômico aliado à preocupação com a produção de qualidade e as exigências do consumidor fizeram aumentar o uso de geotecnologias como sensoriamento remoto – SR – e Global Navigation Satellite Systems – GNSS – na vitivinicultura , surgindo a vitivinicultura de precisão. Entre os sensores remotos utilizados , podem ser destacados os espectroradiômetros utilizados para conhecer os espectros de reflectância que carregam informações que , potencialmente , são úteis, pois fornecem dados não destrutivos, econômicos e praticamente em tempo real. A pesquisa desenvolvida tem como objetivo geral investigar a resposta espectral de folhas, ramos , solo e rochas medidos em dois vinhedos , sua relação com a localização e resultados de análises químicas de folhas e solos dos mesmos vinhedos. O estudo foi realizado, na Região da Serra Gaúcha, no município de Nova Pádua, que se localiza à latitude 29º01'43" sul e à longitude 51º18'24" oeste, em altitudes que variam de 600 a 800 metros. Nas áreas estudadas foram escolhidas dez parcelas com as castas viníferas tintas Cabernet Sauvignon e Merlot devido à sua importância para a produção regional de vinhos finos. Foram recolhidas amostras de solo em dez parcelas de videiras selecionadas. Para obter as coordenadas precisas dos vinhedos realizou - se o georreferenciamento com a utilização um par de receptores GNSS de dupla frequência (L1/L2). Além do uso de GNSS, foi realizado um voo com veículo aéreo não tripulado ( VANT), usando duas câmeras, uma delas operando no RGB e a outra com sensitividade no infravermelho, permitindo a obtenção de índice s de vegetação como o NDVI. Os dados de campo consistiram em espectros de reflectância no domínio espectral entre 350 nm e 2500 nm medidos em solos e em folhas e ramos de videiras. O s espectros foram processados no software ViewSpec Pro, organizados em tabelas e analisados estatisticamente. A avaliação das diferenças entre as médias foi realizada pela aplicação da ANOVA, considerando o nível de 5% de significância. As técnicas multivariadas de Análise de Componentes Principais e Aná lise Discriminante foram executadas em dados previamente auto escalonados. Solos das parcelas e folhas de videiras foram coletadas para análise química efetuada nos laboratórios da Universidade Federal do Rio Grande do Sul – UFRGS, sendo determinadas as concentrações dos elementos N, P, K, Ca, Mg, S, Cu, Zn, Fe, Mn e B nas folhas , e de 21 parâmetros de solo (elemen tos químicos e atributos agronômicos ) para cada uma das parcelas estudadas. Diferenças químicas significativas a um nível de confiança de 95% entre as duas áreas estudadas foram encontradas para seis atributos do solo, e os espectros de refletância médios foram separados por este mesmo nível ao longo da maior parte do domínio espectral observado. Correlações entre concentrações e reflectâncias para alguns domínios de comprimentos de onda foram encontradas, e análises por Partial Least Square Regression para dados de folhas e solos apresentaram coeficientes de correlação de Pearson r > 0,8. A análise discriminante aplicada aos dados de reflectância dos ramos das videiras para a separabilidade entre vinhedos e entre as variedades de uvas , alcançando acurácia superior a 90% . Como conclusões tem - se que o teor mineral de folhas e solos influenciam as respectivas reflectâncias, e quando considerados diferentes locais sua separação é possível. Os métodos relatados podem contribuir para a melhoria e consolidação das normas para Indicações Geográficas

A machine learning-remote sensing framework for modelling water stress in Shiraz vineyards

Thesis (MA)--Stellenbosch University, 2018.ENGLISH ABSTRACT: Water is a limited natural resource and a major environmental constraint for crop production in viticulture. The unpredictability of rainfall patterns, combined with the potentially catastrophic effects of climate change, further compound water scarcity, presenting dire future scenarios of undersupplied irrigation systems. Major water shortages could lead to devastating loses in grape production, which would negatively affect job security and national income. It is, therefore, imperative to develop management schemes and farming practices that optimise water usage and safeguard grape production. Hyperspectral remote sensing techniques provide a solution for the monitoring of vineyard water status. Hyperspectral data, combined with the quantitative analysis of machine learning ensembles, enables the detection of water-stressed vines, thereby facilitating precision irrigation practices and ensuring quality crop yields. To this end, the thesis set out to develop a machine learning–remote sensing framework for modelling water stress in a Shiraz vineyard. The thesis comprises two components. Component one assesses the utility of terrestrial hyperspectral imagery and machine learning ensembles to detect water-stressed Shiraz vines. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensembles were employed to discriminate between water-stressed and non-stressed Shiraz vines. Results showed that both ensemble learners could effectively discriminate between water-stressed and non-stressed vines. When using all wavebands (p = 176), RF yielded a test accuracy of 83.3% (KHAT = 0.67), with XGBoost producing a test accuracy of 80.0% (KHAT = 0.6). Component two explores semi-automated feature selection approaches and hyperparameter value optimisation to improve the developed framework. The utility of the Kruskal-Wallis (KW) filter, Sequential Floating Forward Selection (SFFS) wrapper, and a Filter-Wrapper (FW) approach, was evaluated. When using optimised hyperparameter values, an increase in test accuracy ranging from 0.8% to 5.0% was observed for both RF and XGBoost. In general, RF was found to outperform XGBoost. In terms of predictive competency and computational efficiency, the developed FW approach was the most successful feature selection method implemented. The developed machine learning–remote sensing framework warrants further investigation to confirm its efficacy. However, the thesis answered key research questions, with the developed framework providing a point of departure for future studies.AFRIKAANSE OPSOMMING: Water is 'n beperkte natuurlike hulpbron en 'n groot omgewingsbeperking vir gewasproduksie in wingerdkunde. Die onvoorspelbaarheid van reënvalpatrone, gekombineer met die potensiële katastrofiese gevolge van klimaatsverandering, voorspel ‘n toekoms van water tekorte vir besproeiingstelsels. Groot water tekorte kan lei tot groot verliese in druiweproduksie, wat 'n negatiewe uitwerking op werksekuriteit en nasionale inkomste sal hê. Dit is dus noodsaaklik om bestuurskemas en boerderypraktyke te ontwikkel wat die gebruik van water optimaliseer en druiweproduksie beskerm. Hyperspectrale afstandswaarnemingstegnieke bied 'n oplossing vir die monitering van wingerd water status. Hiperspektrale data, gekombineer met die kwantitatiewe analise van masjienleer klassifikasies, fasiliteer die opsporing van watergestresde wingerdstokke. Sodoende verseker dit presiese besproeiings praktyke en kwaliteit gewasopbrengs. Vir hierdie doel het die tesis probeer 'n masjienleer-afstandswaarnemings raamwerk ontwikkel vir die modellering van waterstres in 'n Shiraz-wingerd. Die tesis bestaan uit twee komponente. Komponent 1 het die nut van terrestriële hiperspektrale beelde en masjienleer klassifikasies gebruik om watergestresde Shiraz-wingerde op te spoor. Die Ewekansige Woud (RF) en Ekstreme Gradiënt Bevordering (XGBoost) algoritme was gebruik om te onderskei tussen watergestresde en nie-gestresde Shiraz-wingerde. Resultate het getoon dat beide RF en XGBoost effektief kan diskrimineer tussen watergestresde en nie-gestresde wingerdstokke. Met die gebruik van alle golfbande (p = 176) het RF 'n toets akkuraatheid van 83.3% (KHAT = 0.67) behaal en XGBoost het 'n toets akkuraatheid van 80.0% (KHAT = 0.6) gelewer. Komponent twee het die gebruik van semi-outomatiese veranderlike seleksie benaderings en hiperparameter waarde optimalisering ondersoek om die ontwikkelde raamwerk te verbeter. Die nut van die Kruskal-Wallis (KW) filter, sekwensiële drywende voorkoms seleksie (SFFS) wrapper en 'n Filter-Wrapper (FW) benadering is geëvalueer. Die gebruik van optimaliseerde hiperparameter waardes het gelei tot 'n toename in toets akkuraatheid (van 0.8% tot 5.0%) vir beide RF en XGBoost. In die algeheel het RF beter presteer as XGBoost. In terme van voorspellende bevoegdheid en berekenings doeltreffendheid was die ontwikkelde FW benadering die mees suksesvolle veranderlike seleksie metode. Die ontwikkelde masjienleer-afstandwaarnemende raamwerk benodig verder navorsing om sy doeltreffendheid te bevestig. Die tesis het egter sleutelnavorsingsvrae beantwoord, met die ontwikkelde raamwerk wat 'n vertrekpunt vir toekomstige studies verskaf.Master

Potential phenotyping methodologies to assess inter- and intravarietal variability and to select grapevine genotypes tolerant to abiotic stress

ReviewPlant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussedinfo:eu-repo/semantics/publishedVersio

Physiology and Biochemistry of Cold-hardy Table Grapevines

Grapes are grown worldwide to produce wine, grape juice and are also popular as fresh table grapes or dried raisins. Due to their nutritional value and importance in the multibillion-dollar wine industry, grapes are considered the most commercially important berry crop. Grape production has primarily concentrated on European wine grapes, Vitis vinifera, in the dry, hot Mediterranean and Central Asian climates. V. vinifera is not cold tolerant enough to endure winter temperatures below -15°C. The introduction of several interspecific hybrids (of both wine and table grape) cultivars in the 20th century and selection of a training system has helped propel the expansion of grapevine cultivation in cooler climates such as the Northeastern US and upper midwestern US states. Training and trellising systems are part of viticultural practices that influence many aspects of grapevine growth and productivity. Especially in cool climates like New Hampshire, choosing an appropriate training system will provide the grapevines with good exposure of leaves and berries to sunlight leading to fruits with improved berry composition and higher levels of sugar accumulation as well as increased concentrations of anthocyanins and phenolic compounds. However, there is limited research on the impact of training systems on cold-hardy table grapevine physiology and biochemistry. To address these knowledge gaps research was conducted at the UNH Woodman Horticultural Research Farm in Durham, NH, where cold-hardy grape varieties are growing on two different training systems. Mars and Canadice grape varieties grown on vertical shoot positioning (VSP) and Munson (M) training systems were used. Grapevine physiology and biochemistry were followed throughout three growing seasons using destructive and non-destructive methods to monitor grapevine health. Additionally, considering the current need for alternative environmentally friendly fungicides, plant material from these cold-hardy grape cultivars was tested for their putative antifungal properties. The objectives of this study were to: (1) Determine the physiological and biochemical parameters of Canadice and Mars cold-hardy grape varieties growing on vertical shoot positioning (VSP) and Munson training systems, and (2) Investigate the putative antifungal activity of field-collected grapevine leaves and cell suspension cultures obtained from Canadice and Mars grapevines against Botrytis cinerea. I hypothesized that the training system would influence the SPAD measurements, spectral indices (normalized difference vegetation index, red edge inflection point, moisture stress index, and phenology index), and gas exchange measurements (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis, transpiration rate, vapor pressure deficit, and water use efficiency) of Mars and Canadice leaves growing on two different training systems. I also hypothesized that the training system would have an effect on the amount of leaf photosynthetic pigments, leaf, juice, and skin metabolomes, titratable acidity and soluble solid contents of Canadice and Mars growing on two different training systems (Chapter 2). I hypothesized that field-collected leaves and cell suspension cultures established from Canadice and Mars grape varieties would contain compounds with antifungal activity against B. cinerea (Chapter 3). For objective 1, physiological parameters were measured with SPAD, spectral analysis, and gas exchange analysis on grapevine leaves throughout three growing seasons (2019, 2020, and 2021). Specifically, I determined the SPAD measurements, the spectral indices normalized difference vegetation index (NDVI), red edge inflection point (REIP), moisture stress index (MSI) and phenology index, and gas exchange measurements to determine intercellular carbon dioxide concentration (Ci), stomatal conductance (gs), vapor pressure deficit (VPD), net photosynthesis (A), transpiration (E), and water use efficiency (WUE). While no differences were found regarding training systems alone, there was a significant interaction of training system with time, suggesting that training system had different effects at different times. For the biochemical parameters, the same leaves that were used to perform SPAD measurements were used to analyze photosynthetic pigments and proton based nuclear magnetic resonance (1H-NMR spectroscopy)-based metabolomics. Consistent with the results of physiological parameters, no differences were found for photosynthetic pigments - chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids - between training systems, but the training system had different effects at different time points. The leaf metabolites studied using 1H-NMR spectroscopy coupled with multivariate statistical analysis did not distinguish samples based on training systems, but sample separation occurred based on phenological stages. The compounds identified showed variations between flowering, veraison, and harvest. Namely, sucrose gradually increased from flowering to harvest. Additionally, the 1H-NMR spectroscopy-based metabolome of grape juice was investigated in grape berries collected from veraison to harvest. Various kinds of metabolites were identified. Fructose, glucose, alanine, threonine, myo-Inositol, and 3-hydroxybutyrate were all shown to increase from veraison to harvest. The amount of fructose and glucose increased over time (between veraison and harvest) and are indicators of berry ripeness. Furthermore, at harvest, grape titratable acidity and total soluble solid content were determined, and berry skin composition was investigated using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) analysis. Distinct sets of metabolites were identified in Mars and Canadice skin samples and were dependent on the training system. For my objective 2, I investigated the putative antifungal activity of Mars- and Canadice-derived products, specifically field-collected grapevine senescent leaves and cell suspension cultures, against B. cinerea. The aim was to gather knowledge that could lead to the development of new botanical fungicides that could be used as an alternative to synthetic fungicides for disease management in vineyards. This approach could contribute to sustainable management practices in the long term. Using grapevine debris (such as canes, wood, and leaves) from V. vinifera to suppress B. cinerea and other plant pathogens has been successfully demonstrated. However, there is limited research evaluating secondary metabolites with antifungal properties from cold-hardy grapevines. Our results show that grapevine-derived extracts have antifungal activity in vitro and in detached berry experiments when challenged with B. cinerea, but the antifungal activity was not translated to in planta experiments. The metabolic profiling of senescent leaves and cell suspension cultures of Mars and Canadice identified an array of compounds, including some reported to have antimicrobial properties. Given the list of compounds that have been identified in cold-hardy grapevine-derived products, future work should examine these unique compounds present in the senescent leaves and cell cultures under controlled experimental conditions. While our results indicated that Mars- and Canadice-derived products have antifungal activity, the materials used in this study were crude extracts. Future studies should focus on using finer grapevine-products to test the efficacy against B. cinerea, not only in vitro, but also using pilot-scale greenhouse trials, and vineyard trials

A review of neural networks in plant disease detection using hyperspectral data

© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data