Assessing Berry Number for Grapevine Yield Estimation by Image Analysis: Case Study with the Red Variety “Syrah”

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoThe yield estimation provides information that help growers to make decisions in order to optimize crop growth and to organize the harvest operations in field and in the cellar. In most vineyard estates yield is forecasted using manual methods. However, image analysis methods, which are less invasive low cost and more representative are now being developed. The main objective of this work was to estimate yield through data obtained in the frame of Vinbot project during the 2019 season. In this thesis, images of the grapevine variety Syrah taken in the laboratory and in the vineyards of the “Instituto Superior de Agronomia” in Lisbon were analyzed. In the laboratory the images were taken manually with an RGB camera, while in the field vines were imaged either manually and by the Vinbot robot. From these images, the number of visible berries were counted with MATLAB. From the laboratory values, the relationships between the number of visible berries and actual bunch weight and berry number were studied. From the data obtained in the field, it was analyzed the visibility of the berries at different levels of defoliation and the relationship between the area of visible bunches and the visible berries. Berry-by-berry occlusion showed a value of 6.4% at pea-size, 14.5% at veraison and 25% at maturation. In addition, high and significant determination coefficient were obtained between actual yield and visible berries. The comparison of estimated yield, obtained using the regression models with actual yield, showed an underestimation at all the three phonological stages. This low accuracy of the developed models show that the use of algorithms based on visible berry number on the images to estimate yield still needs further researchN/

A systematic literature review

Barriguinha, A., Neto, M. D. C., & Gil, A. (2021). Vineyard yield estimation, prediction, and forecasting: A systematic literature review. Agronomy, 11(9), 1-27. [1789]. https://doi.org/10.3390/agronomy11091789Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.publishersversionpublishe

Vineyard yield estimation using image analysis – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation is one of the main goals of the wine industry, this because with an accurate yield estimation it is possible to have a significant reduction in production costs and a better management of the wine industry. Traditional methods for yield estimation are laborious and time consuming, for these reasons in the last years we are witnessing to the development of new methodologies, most of which are based on image analysis. Thanks to the continuous updating and improvement of the computer vision techniques and of the robotic platforms, image analysis applied to the yield estimation is becoming more and more efficient. In fact the results shown by the different studies are very satisfying, at least as regards the estimation of what is possible to see, while are under development several procedures which have the objective to estimate what is not possible to see, due to bunch occlusion by leaves and by others clusters. I this work the different methodologies and the different approaches used for yield estimation are described, including both traditional methods and new approaches based on image analysis, in order to present the advantages and disadvantages of each of themN/

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

High-throughput phenotyping of yield parameters for modern grapevine breeding

Weinbau wird auf 1% der deutschen Agrarfläche betrieben. Auf dieser vergleichsweise kleinen Anbaufläche wird jedoch ein Drittel aller in der deutschen Landwirtschaft verwendeten Fungizide appliziert, was auf die Einführung von Schaderregern im 19. Jahrhundert zurück zu führen ist. Für einen nachhaltigen Anbau ist eine Reduktion des Pflanzenschutzmittelaufwands dringend notwendig. Dieses Ziel kann durch die Züchtung und den Anbau neuer, pilzwiderstandsfähiger Rebsorten erreicht werden. Die Rebenzüchtung als solche ist sehr zeitaufwendig, da die Entwicklung neuer Rebsorten 20 bis 25 Jahre dauert. Der Einsatz der markergestützten Selektion (MAS) erhöht die Effizienz der Selektion in der Rebenzüchtung fortwährend. Eine weitere Effizienzsteigerung ist mit der andauernden Verbesserung der Hochdurchsatz Genotypisierung zu erwarten. Im Vergleich zu den Methoden der Genotypisierung ist die Qualität, Objektivität und Präzision der traditionellen Phänotypisierungsmethoden begrenzt. Die Effizienz in der Rebenzüchtung soll mit der Entwicklung von Hochdurchsatz Methoden zur Phänotypisierung durch sensorgestützte Selektion weiter gesteigert werden. Hierfür sind bisher vielfältige Sensortechniken auf dem Markt verfügbar. Das Spektrum erstreckt sich von RGB-Kameras über Multispektral-, Hyperspektral-, Wärmebild- und Fluoreszenz- Kameras bis hin zu 3D-Techniken und Laserscananwendungen. Die Phänotypisierung von Pflanzen kann unter kontrollierten Bedingungen in Klimakammern oder Gewächshäusern beziehungsweise im Freiland stattfinden. Die Möglichkeit einer standardisierten Datenaufnahme nimmt jedoch kontinuierlich ab. Bei der Rebe als Dauerkultur erfolgt die Aufnahme äußerer Merkmale, mit Ausnahme junger Sämlinge, deshalb auch überwiegend im Freiland. Variierende Lichtverhältnisse, Ähnlichkeit von Vorder- und Hintergrund sowie Verdeckung des Merkmals stellen aus methodischer Sicht die wichtigsten Herausforderungen in der sensorgestützen Merkmalserfassung dar. Bis heute erfolgt die Aufnahme phänotypischer Merkmale im Feld durch visuelle Abschätzung. Hierbei werden die BBCH Skala oder die OIV Deskriptoren verwendet. Limitierende Faktoren dieser Methoden sind Zeit, Kosten und die Subjektivität bei der Datenerhebung. Innerhalb des Züchtungsprogramms kann daher nur ein reduziertes Set an Genotypen für ausgewählte Merkmale evaluiert werden. Die Automatisierung, Präzisierung und Objektivierung phänotypischer Daten soll dazu führen, dass (1) der bestehende Engpass an phänotypischen Methoden verringert, (2) die Effizienz der Rebenzüchtung gesteigert, und (3) die Grundlage zukünftiger genetischer Studien verbessert wird, sowie (4) eine Optimierung des weinbaulichen Managements stattfindet. Stabile und über die Jahre gleichbleibende Erträge sind für eine Produktion qualitativ hochwertiger Weine notwendig und spielen daher eine Schlüsselrolle in der Rebenzüchtung. Der Fokus dieser Studie liegt daher auf Ertragsmerkmalen wie der Beerengröße, Anzahl der Beeren pro Traube und Menge der Trauben pro Weinstock. Die verwandten Merkmale Traubenarchitektur und das Verhältnis von generativem und vegetativem Wachstum wurden zusätzlich bearbeitet. Die Beurteilung von Ertragsmerkmalen auf Einzelstockniveau ist aufgrund der genotypischen Varianz und der Vielfältigkeit des betrachteten Merkmals komplex und zeitintensiv. Als erster Schritt in Richtung Hochdurchsatz (HT) Phänotypisierung von Ertragsmerkmalen wurden zwei voll automatische Bildinterpretationsverfahren für die Anwendung im Labor entwickelt. Das Cluster Analysis Tool (CAT) ermöglicht die bildgestützte Erfassung der Traubenlänge, -breite und -kompaktheit, sowie der Beerengröße. Informationen über Anzahl, Größe (Länge, Breite) und das Volumen der einzelnen Beeren liefert das Berry Analysis Tool (BAT). Beide Programme ermöglichen eine gleichzeitige Erhebung mehrerer, präziser phänotypischer Merkmale und sind dabei schnell, benutzerfreundlich und kostengünstig. Die Möglichkeit, den Vorder- und Hintergrund in einem Freilandbild zu unterscheiden, ist besonders in einem frühen Entwicklungsstadium der Rebe aufgrund der fehlenden Laubwand schwierig. Eine Möglichkeit, die beiden Ebenen in der Bildanalyse zu trennen, ist daher unerlässlich. Es wurde eine berührungsfreie, schnelle sowie objektive Methode zur Bestimmung des Winterschnittholzgewichts, welches das vegetative Wachstum der Rebe beschreibt, entwickelt. In einem innovativen Ansatz wurde unter Kombination von Tiefenkarten und Bildsegmentierung die sichtbare Winterholzfläche im Bild bestimmt. Im Zuge dieser Arbeit wurde die erste HT Phänotypisierungspipeline für die Rebenzüchtung aufgebaut. Sie umfasst die automatisierte Bildaufnahme im Freiland unter Einsatz des PHENObots, das Datenmanagement mit Datenanalyse sowie die Interpretation des erhaltenen phänotypischen Datensatzes. Die Basis des PHENObots ist ein automatisiert gesteuertes Raupenfahrzeug. Des Weiteren umfasst er ein Multi-Kamera- System, ein RTK-GPS-System und einen Computer zur Datenspeicherung. Eine eigens entwickelte Software verbindet die Bilddaten mit der Standortreferenz. Diese Referenz wird anschließend für das Datenmanagement in einer Datenbank verwendet. Um die Funktionalität der Phänotypisierungspipeline zu demonstrieren, wurden die Merkmale Beerengröße und -farbe im Rebsortiment des Geilweilerhofes unter Verwendung des Berries In Vineyard (BIVcolor) Programms erfasst. Im Durschnitt werden 20 Sekunden pro Weinstock für die Bildaufnahme im Feld benötigt, gefolgt von der Extraktion der Merkmale mittels automatischer, objektiver und präziser Bildauswertung. Im Zuge dieses Versuches konnten mit dem PHENObot 2700 Weinstöcke in 12 Stunden erfasst werden, gefolgt von einer automatischen Bestimmung der Merkmale Beerengröße und -farbe aus den Bildern. Damit konnte die grundsätzliche Machbarkeit bewiesen werden. Diese Pilotpipeline bietet nun die Möglichkeit zur Entwicklung weiterer innovativer Programme zur Erhebung neuer Merkmale sowie die Integration zusätzlicher Sensoren auf dem PHENObot.Grapevine is grown on about 1% of the German agricultural area requiring one third of all fungicides sprayed due to pathogens being introduced within the 19th century. In spite of this requirement for viticulture a reduction is necessary to improve sustainability. This objective can be achieved by growing fungus resistant grapevine cultivars. The development of new cultivars, however, is very time-consuming, taking 20 to 25 years. In recent years the breeding process could be increased considerably by using marker assisted selection (MAS). Further improvements of MAS applications in grapevine breeding will come along with developing of faster and more cost efficient high-throughput (HT) genotyping methods.Complementary to genotyping techniques the quality, objectivity and precision of current phenotyping methods is limited and HT phenotyping methods need to be developed to further increase the efficiency of grapevine breeding through sensor assisted selection. Many different types of sensors technologies are available ranging from visible light sensors (Red Green Blue (RGB) cameras), multispectral, hyperspectral, thermal, and fluorescence cameras to three dimensional (3D) camera and laser scan approaches. Phenotyping can either be done under controlled environments (growth chamber, greenhouse) or can take place in the field, with a decreasing level of standardization. Except for young seedlings, grapevine as a perennial plant needs ultimately to be screened in the field. From a methodological point of view a variety of challenges need to be considered like the variable light conditions, the similarity of fore- and background, and in the canopy hidden traits.The assessment of phenotypic data in grapevine breeding is traditionally done directly in the field by visual estimations. In general the BBCH scale is used to acquire and classify the stages of annual plant development or OIV descriptors are applied to assess the phenotypes into classes. Phenotyping is strongly limited by time, costs and the subjectivity of records. Therefore, only a comparably small set of genotypes is evaluated for certain traits within the breeding process. Due to that limitation, automation, precision and objectivity of phenotypic data evaluation is crucial in order to (1) reduce the existing phenotyping bottleneck, (2) increase the efficiency of grapevine breeding, (3) assist further genetic studies and (4) ensure improved vineyard management. In this theses emphasis was put on the following aspects: Balanced and stable yields are important to ensure a high quality wine production playing a key role in grapevine breeding. Therefore, the main focus of this study is on phenotyping different parameters of yield such as berry size, number of berries per cluster, and number of clusters per vine. Additionally, related traits like cluster architecture and vine balance (relation between vegetative and generative growth) were considered. Quantifying yield parameters on a single vine level is challenging. Complex shapes and slight variations between genotypes make it difficult and very time-consuming.As a first step towards HT phenotyping of yield parameters two fully automatic image interpretation tools have been developed for an application under controlled laboratory conditions to assess individual yield parameters. Using the Cluster Analysis Tool (CAT) four important phenotypic traits can be detected in one image: Cluster length, cluster width, berry size and cluster compactness. The utilization of the Berry Analysis Tool (BAT) provides information on number, size (length and width), and volume of grapevine berries. Both tools offer a fast, user-friendly and cheap procedure to provide several precise phenotypic features of berries and clusters at once with dimensional units in a shorter period of time compared to manual measurements.The similarity of fore- and background in an image captured under field conditions is especially difficult and crucial for image analysis at an early grapevine developmental stage due to the missing canopy. To detect the dormant pruning wood weight, partly determining vine balance, a fast and non-invasive tool for objective data acquisition in the field was developed. In an innovative approach it combines depth map calculation and image segmentation to subtract the background of the vine obtaining the pruning area visible in the image. For the implementation of HT field phenotyping in grapevine breeding a phenotyping pipeline has been set up. It ranges from the automated image acquisition directly in the field using the PHENObot, to data management, data analysis and the interpretation of obtained phenotypic data for grapevine breeding aims. The PHENObot consists of an automated guided tracked vehicle system, a calibrated multi camera system, a Real-Time-Kinematic GPS system and a computer for image data handling. Particularly developed software was applied in order to acquire geo referenced images directly in the vineyard. The geo-reference is afterwards used for the post-processing data management in a database. As phenotypic traits to be analysed within the phenotyping pipeline the detection of berries and the determination of the berry size and colour were considered. The highthroughput phenotyping pipeline was tested in the grapevine repository at Geilweilerhof to extract the characteristics of berry size and berry colour using the Berries In Vineyards (BIVcolor) tool. Image data acquisition took about 20 seconds per vine, which afterwards was followed by the automatic image analysis to extract objective and precise phenotypic data. In was possible to capture images of 2700 vines within 12 hours using the PHENObot and subsequently automatic analysis of the images and extracting berry size and berry colour. With this analysis proof of principle was demonstrated. The pilot pipeline providesthe basis for further development of additional evaluation modules as well as the integration of other sensors

Grapevine yield estimation using image analysis for the variety Arinto

Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation can lead to difficulties in the vineyard and winery, if it is done inaccurately following wrong procedures, doing a non-representative sampling or for the human error. Moreover, the traditional yield estimation methods are time consuming and destructive because they need someone that goes into the vineyard to count the yield components and that take out from the vineyard inflorescence or bunches to count and weight the flowers and the berries. To avoid these problems and the errors that can occur on this way, the development and application of new and innovative techniques to estimate the yield through the analysis of RGB images taken under field conditions are under study from different groups of research. In our research work we’ve studied the application of counting the yield components in the images throughout all the growing season. Furthermore, we’ve studied two different algorithms that starting from the survey of canopy porosity and/or visible bunches area, can help to do an estimation of the yield. The most promising yield estimation, based on the counting of the yield components done through image analysis, was found to be at the phenological stage of four leaves out, which shown a mean absolute percent error (MA%E) of 32 ± 2% and an correlaion coefficient (r Obs,Est) between observed and estimated shoots of 0.62. The two algorithms used different models: for estimating the area of the bunches covered by leaves and to estimate the weight of the bunches per linear canopy meter. When the area of the bunches without leaf occlusion was estimated, an average percentage of occlusion generated by the bunches on the other bunches of 8%, 6% and 12% respectively at pea size, veraison and maturation, was used to estimate the total area of the bunches. When the total area of the bunches per linear canopy meter was estimated the two models to estimate the grape weight were used. Finally, to estimate the weight at harvest, the growth factors of 6.6 and 1.7 respectively, at pea size and veraison were used. The first algorithm shown a MA%E, between the estimated and observed values of yield, of - 33.59%, -9.24% and -11.25%, instead the second algorithm shown a MA%E of -6.81%, -1.35% and 0.01% respectively at pea-size, veraison and maturationN/

PHENOquad: A new multi sensor platform for field phenotyping and screening of yield relevant characteristics within grapevine breeding research

Balanced and stable yield is a major trait in grapevine breeding and breeding research. Grapevine yield hereby is a complex quantitative trait, as it is influenced by multiple plant parameters, like berry size, number of berries per bunch, number of bunches per shoot, management, and environmental factors. In the current breeding process, the complexity of this trait has shown that a classification according to descriptive factors for marker development is only possible to a limited extent. Precise field phenotyping of yield-related traits is the basic prerequisite to be able to measure such quantitative traits. This, however, is the major bottleneck due to labor, time and constrains of plant material in the breeding process. For this reason, one of our main goals with the newly developed phenotyping platform PHENOquad with its multisensor system PHENOboxx is to improve phenotyping efficiency of grapevine yield to overcome the phenotyping bottleneck. The newly developed embedded vision system PHENOboxx is mounted on an "all-terrain vehicle (ATV)". This allows a fast data acquisition on a large number of individual vines. In order to evaluate the yield potential of breeding material in comparison to established grapevine cultivars, various yield-related parameters of the vines are quantified directly in the field with high spatial and temporal resolution. As key parameters for yield-related phenotyping, the number of shoots, bunches, berries and the weight of dormant pruning wood was identified. The image data acquired are annotated to train the artificial intelligence (AI). Within the process, the image analysis results are compared to annotated ground truth data and correlated with the field reference data. We expect to increase the precision, target specificity and throughput of screening grapevine material without reducing its accuracy over time by using the PHENOquad. In addition, a weighting of yield-relevant parameters would be possible. This opens up new possibilities for efficient plant evaluation in the scope of grapevine breeding. Also new application possibilities for precision viticulture are conceivable

Detection of Grape Clusters in Images using Convolutional Neural Network

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple Computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don’t address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes

Development of a new non-invasive vineyard yield estimation method based on image analysis

Doutoramento em Engenharia Agronómica / Instituto Superior de Agronomia. Universidade de LisboaPredicting vineyard yield with accuracy can provide several advantages to the whole vine and wine industry. Today this is majorly done using manual and sometimes destructive methods, based on bunch samples. Yield estimation using computer vision and image analysis can potentially perform this task extensively, automatically, and non-invasively. In the present work this approach is explored in three main steps: image collection, occluded fruit estimation and image traits conversion to mass. On the first step, grapevine images were collected in field conditions along some of the main grapevine phenological stages. Visible yield components were identified in the image and compared to ground truth. When analyzing inflorescences and bunches, more than 50% were occluded by leaves or other plant organs, on three cultivars. No significant differences were observed on bunch visibility after fruit set. Visible bunch projected area explained an average of 49% of vine yield variation, between veraison and harvest. On the second step, vine images were collected, in field conditions, with different levels of defoliation intensity at bunch zone. A regression model was computed combining canopy porosity and visible bunch area, obtained via image analysis, which explained 70-84% of bunch exposure variation. This approach allowed for an estimation of the occluded fraction of bunches with average errors below |10|%. No significant differences were found between the model’s output at veraison and harvest. On the last step, the conversion of bunch image traits into mass was explored in laboratory and field conditions. In both cases, cultivar differences related to bunch architecture were found to affect weight estimation. A combination of derived variables which included visible bunch area, estimated total bunch area, visible bunch perimeter, visible berry number and bunch compactness was used to estimate yield on undisturbed grapevines. The final model achieved a R2 = 0.86 between actual and estimated yield (n = 213). If performed automatically, the final approach suggested in this work has the potential to provide a non-invasive method that can be performed accurately across whole vineyards.N/

Thermography to assess grapevine status and traits opportunities and limitations in crop monitoring and phenotyping – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoClimate change and the increasing water shortage pose increasing challenges to agriculture and viticulture, especially in typically dry and hot areas such as the Mediterranean and demand for solutions to use water resources more effectively. For this reason, new tools are needed to precisely monitor water stress in crops such as grapevine in order to save irrigation water, while guaranteeing yield. Imaging technologies and remote sensing tools are becoming more common in agriculture and plant/crop science research namely to perform phenotyping/selection or for crop stress monitoring purposes. Thermography emerged as important tool for the industry and agriculture. It allows detection of the emitted infrared thermal radiation and conversion of infrared radiation into temperature distribution maps. Considering that leaf temperature is a feasible indicator of stress and/or stomatal behavior, thermography showed to be capable to support characterization of novel genotypes and/or monitor crop’s stress. However, there are still limitations in the use of the technique that need to be minimized such as the accuracy of thermal data due to variable weather conditions, limitations due to the high costs of the equipment/platforms and limitations related to image analysis and processing to extract meaningful thermal data. This work revises the role of remote sensing and imaging in modern viticulture as well as the advantages and disadvantages of thermography and future developments, focusing on viticultureN/