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

A street-point method to measure the spatiotemporal relationship between walkability and pedestrian flow

Jardim, B., Neto, M. D. C., & Barriguinha, A. (2023). A street-point method to measure the spatiotemporal relationship between walkability and pedestrian flow. Computers, Environment and Urban Systems, 104(September), [101993]. https://doi.org/10.1016/j.compenvurbsys.2023.101---This research was funded by the Project C-TECH—Climate Driven Technologies for Low Carbon Cities, grant number POCI-01-0247-FEDER-045919|LISBOA-01-0247-FEDER-045919, co-financed by the ERDF—European Regional Development Fund through the Operational Program for Competitiveness and Internationalization—COMPETE 2020, the Lisbon Portugal Regional Operational Program—LISBOA 2020 and by the Portuguese Foundation for Science and Technology—FCT under MIT Portugal Program. This work was also supported by Portuguese national funds through the Portuguese Foundation for Science and Technology—FCT under research grant FCT UIDB/04152/2020–Centro de Investigação em Gestão de Informação (MagIC).Walkability indicators are a pivotal method to evaluate the role of the built environment in peoples' decisions regarding active mobility, supporting the application of public measures that contribute to more sustainable and resilient regions. Currently, data used to evaluate associations between walkability indicators and travel behavior is obtained via traditional methods of data collection, like questionnaires, that are costly and hard to scale in large urban environments. Moreover, the spatial resolution of most indicators may not be sufficient to support granular local public interventions. To face these issues, we propose a novel walkability indicator that provides a score of walkability for every one-meter street point, based on street conditions and accessibility to points of interest calculated with a Cumulative-Gaussian impedance function. Resorting to Linear and Geospatial Weighted Regressions, we evaluate the associations between walkability features and pedestrian flow data retrieved from mobile phone communication signals for a week in March 2022. The relationship between walkability features and pedestrian flow is stronger during workdays, in which accessibility to education, food amenities and government services are the most important predictors. On the weekend, the features with more explanatory power are accessibility to crosswalks and leisure amenities. Accessibility to public transport, sidewalk width and slope seem to impact pedestrian decisions independently of the day type, although the impact is stronger on weekends. This study provides policy makers and urban planners with a practical tool to effectively support the evaluation of current street conditions and access areas that are underserved, as well as plan and gauge new local interventions, while objectively understanding their impacts on pedestrian mobility.publishersversionepub_ahead_of_prin

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

An intermunicipal integrated analytical territorial intelligence platform

Simões, P., de Castro Neto, M., Sarmento, P., & Barriguinha, A. (2023). Oeste smart region: An intermunicipal integrated analytical territorial intelligence platform. Mapping, 32(211), 50-61. [5]. https://doi.org/10.59192/mapping.395---This work was funded by the European Union under the European Regional Development Fund through the financing programs Compete 2020 and Portugal 2020.Smart regions are described as an instrument to achieve sustainable planning at the regional level, promoting knowledge-based development through learning as an integral part of the development of regional resources that solves challenges through the knowledgeable application of new technologies, the organization of processes and reasonable and future-proof decision-making. With this work we intend to present a territorial intelligence platform, in particular the spatial data infrastructure that supports it. Based on the potential of multiple sources and formats of data available (Big Data), from the systems of twelve Portuguese municipalities. Along with the Internet of Things and collective intelligence the developed model, sets out as an ambition to take advantage of the potential of data science and artificial intelligence, to promote a regional model of governance based on the management of information capable of leveraging the creation of a territorial intelligence center constituting a new paradigm of territorial planning and management based on facts. ___ Las regiones inteligentes se describen como un instrumento para lograr una planificación sostenible a nivel regional, promoviendo el desarrollo basado en el conocimiento a través del aprendizaje continuo como parte integral del desarrollo de los recursos regionales que resuelve los desafíos a través de la aplicación con conocimiento de las nuevas tecnologías, la organización de procesos y toma de decisiones razonables y preparadas para el futuro. Con este trabajo pretendemos presentar una plataforma de inteligencia territorial, en particular la infraestructura de datos espaciales que la soporta. Basado en el potencial de múltiples fuentes y formatos de datos disponibles (Big Data), de los sistemas de doce municipios portugueses. Junto con el Internet de las Cosas y la inteligencia colectiva, el modelo desarrollado se plantea como una ambición de aprovechar el potencial de la ciencia de datos y la inteligencia artificial, para impulsar un modelo regional de gobernanza basado en la gestión de la información capaz de impulsar la creación de un centro de inteligencia territorial que constituye un nuevo paradigma de planificación y gestión territorial basada en hechos.publishersversionpublishe

Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Barriguinha, A., Jardim, B., De Castro Neto, M., & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation, 114(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. 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 study was conducted in the Douro Demarcated Region in Portugal over the period 2016–2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.publishersversionpublishe

Utilisation d'un robot terrestre pour estimer les caracteristiques de la canopee et le rendement au vignoble

This paper aims to present some results of the EU VINBOT (Autonomous cloud-computing vineyard robot to optimize yield management and wine quality) project focused on vineyard yield estimation. A ground truth evaluation trial was set up in an experimental vineyard with two plots of the white varieties ‘Alvarinho’ and ‘Arinto’, trained on a vertical shoot positioning system and spur pruned. For each varietal plot, six smart points were selected with 10 contiguous vines each. During the ripening period of the 2016 season the vines were manually assessed for canopy dimensions and yield and then scanned by the VINBOT sensor head composed with a 2D laser rangefinder, a Kinect v2 camera and a set of robot navigation sensors. Ground truth data was used to compare with the canopy data estimated by the rangefinder and with the output of the image analysis algorithms. Regarding canopy features (height, volume and exposed leaf area), in general an acceptable fit between actual and estimated values was observed with canopy height showing the best agreement. The regression analysis between actual and estimated values of canopy features showed a significant linear relationship for all the features however the lower values of the R2 indicate a weak relationship. Regarding the yield, despite the significant R2 (0.31) showed by the regression analysis between actual and estimated values, the equation of the fitted line indicate that the VINBOT algorithms underestimated the yield by an additive factor. Our results showed that canopy features can be estimated by the VINBOT platform with an acceptable accuracy. However, the underestimation of actual yield, caused mainly by bunch occlusion, deserves further research to improve the algorithms accuracyN/

VINBOT - um robô terrestre para viticultura de precisão

I Congresso Luso-Brasileiro de Horticultura. Sessão ViticulturaNos últimos anos tem-se verificado um aumento exponencial da utilização de robôs na agricultura, estando a União Europeia a fomentar fortemente a investigação nesta área através da Agenda de Investigação Estratégica para a Robótica na Europa. A grande importância do conhecimento da variabilidade espacial do vigor e produção em Viticultura, tem levado ao desenvolvimento de ferramentas de Viticultura de Precisão baseadas em sensores diversos montados em veículos autónomos. Neste trabalho apresentam-se alguns resultados de validação de campo obtidos no âmbito do projeto europeu VINBOT (“Autonomous cloud-computing vineyard robot to optimise yield management and wine quality”) que teve por objectivo o desenvolvimento de um robô terrestre, equipado com diversas câmaras e sensores, para obtenção de mapas de variabilidade espacial quer de características da sebe quer da produção de uma parcela de vinha. O ensaio de validação decorreu numa vinha experimental do Instituto Superior de Agronomia, onde vários segmentos de várias linhas da casta Alvarinho foram rotuladas, submetidas ao registo manual das características da sebe e da produção e, simultaneamente, monitorizados pela plataforma de sensores do robô. Os dados obtidos manualmente foram comparados com os valores estimados pelos algoritmos de análise de imagem do robô. Relativamente às dimensões da sebe os resultados de validação mostram um bom ajustamento entre dados observados e as estimativas proporcionadas pela reconstrução 3D da sebe através do sensor laser “Range Finder”. No que se refere à produção, verificou-se uma ligeira subestimativa resultante da oclusão de alguns cachos quer por outros cachos quer pela vegetação da videira. Estão em curso novos ensaios com vista a testar o robô noutras castas e sistemas de condução e a melhorar os algoritmos de análise de imageminfo:eu-repo/semantics/publishedVersio

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

Purpose—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

ECO@GRO Digital: Uma ferramenta WebGIS de apoio na consultadoria e gestão agro-florestal

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação GeográficaOs Sistemas de Informação Geográfica (SIG) assumem-se cada vez mais como ferramentas de grande utilidade no seio das empresas ligadas à consultadoria e gestão agro-florestal. Facilitam determinadas operações de planeamento, gestão e controlo, constituindo-se como importantes ferramentas de apoio à decisão, permitindo alcançar benefícios de eficiência, eficácia e vantagens competitivas sobre as suas concorrentes. Os proprietários florestais e clientes deste tipo de empresas têm, por vezes, alguma dificuldade em dispor deste tipo de ferramentas. O elevado custo das licenças e a especificidade de utilização a nível técnico, constituem dificuldades na criação e manutenção dos seus dados geográficos. A Internet constitui-se como um meio privilegiado na disponibilização de grandes quantidades de informação geográfica, tornando possível o acesso, por parte dos utilizadores a funcionalidades SIG a partir do seu browser. A utilização de SIG distribuídos na Internet (WebGIS), nomeadamente nas suas vertentes livres de Freeware e OpenSource, pode assumir particular relevância como forma de os clientes destas empresas poderem usufruir de um serviço de grande utilidade, ao aceder a todo um conjunto de informação espacial relevante na sua gestão a custos reduzidos e com conhecimentos limitados de sistemas de informação geográfica.(...