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Details

Details

  • Name

    Mário Cunha
  • Role

    Senior Researcher
  • Since

    01st February 2018
021
Publications

2024

A New Approach for Element Characterization of Grapevine Tissue with Laser-Induced Breakdown Spectroscopy

Authors
Tosin R.; Monteiro-Silva F.; Martins R.; Cunha M.;

Publication
Horticulturae

Abstract
The determination of grape quality parameters is intricately linked to the mineral composition of the fruit; this relationship is increasingly affected by the impacts of climate change. The conventional chemical methodologies employed for the mineral quantification of grape tissues are expensive and impracticable for widespread commercial applications. This paper utilized Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the mineral constituents within the skin, pulp, and seeds of two distinct Vitis vinifera cultivars: a white cultivar (Loureiro) and a red cultivar (Vinhão). The primary objective was to discriminate the potential variations in the calcium (Ca), magnesium (Mg), and nitrogen (N) concentrations and water content among different grape tissues, explaining their consequential impact on the metabolic constitution of the grapes and, by extension, their influence on various quality parameters. Additionally, the study compared the mineral contents of the white and red grape cultivars across three distinct time points post veraison. Significant differences (p < 0.05) were observed between the Loureiro and Vinhão cultivars in Ca concentrations across all the dates and tissues and for Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin and pulp. In the Vinhão cultivar, Ca differences were found in the pulp across the dates, N in the seeds, and water content in the skin, pulp, and seeds. Comparing the cultivars within tissues, Ca exhibited differences in the pulp, Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin, pulp, and seeds. These findings provide insights into the relationship between the grape mineral and water content, climatic factors, and viticulture practices within a changing climate.

2023

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Authors
Magalhaes, SC; Castro, L; Rodrigues, L; Padilha, TC; de Carvalho, F; dos Santos, FN; Pinho, T; Moreira, G; Cunha, J; Cunha, M; Silva, P; Moreira, AP;

Publication
IEEE SENSORS JOURNAL

Abstract
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.

2023

Assessing the resilience of ecosystem functioning to wildfires using satellite-derived metrics of post-fire trajectories

Authors
Marcos, B; Goncalves, J; Alcaraz Segura, D; Cunha, M; Honrado, JP;

Publication
REMOTE SENSING OF ENVIRONMENT

Abstract
Wildfire disturbances can profoundly impact many aspects of both ecosystem functioning and resilience. This study proposes a satellite-based approach to assess ecosystem resilience to wildfires based on post-fire trajec-tories of four key functional dimensions of ecosystems related to carbon, water, and energy exchanges: (i) vegetation primary production; (ii) vegetation and soil water content; (iii) land surface albedo; and (iv) land surface sensible heat. For each dimension, several metrics extracted from satellite image time-series, at the short, medium and long-term, describe both resistance (the ability to withstand environmental disturbances) and re-covery (the ability to pull back towards equilibrium). We used MODIS data for 2000-2018 to analyze trajectories after the 2005 wildfires in NW Iberian Peninsula. Primary production exhibited low resistance, with abrupt breaks immediately after the fire, but rapid recoveries, starting within six months after the fire and reaching stable pre-fire levels two years after. Loss of water content after the fire showed slightly higher resistance but slower and more gradual recoveries than primary production. On the other hand, albedo exhibited varying levels of resistance and recovery, with post-fire breaks often followed by increases to levels above pre-fire within the first two years, but sometimes with effects that persisted for many years. Finally, wildfire effects on sensible heat were generally more transient, with effects starting to dissipate after one year and overall rapid recoveries. Our approach was able to successfully depict key features of post-fire processes of ecosystem functioning at different timeframes. The added value of our multi-indicator approach for analyzing ecosystem resilience to wildfires was highlighted by the independence and complementarity among the proposed indicators targeting four dimensions of ecosystem functioning. We argue that such approaches can provide an enhanced characterization of ecosystem resilience to disturbances, ultimately upholding promising implications for post-fire ecosystem management and targeting different dimensions of ecosystem functioning.

2023

Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops

Authors
Rodrigues, L; Magalhaes, SA; da Silva, DQ; dos Santos, FN; Cunha, M;

Publication
AGRONOMY-BASEL

Abstract
The efficiency of agricultural practices depends on the timing of their execution. Environmental conditions, such as rainfall, and crop-related traits, such as plant phenology, determine the success of practices such as irrigation. Moreover, plant phenology, the seasonal timing of biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, and management conditions. Therefore, assessing the timing the of crops' phenological events and their spatiotemporal variability can improve decision making, allowing the thorough planning and timely execution of agricultural operations. Conventional techniques for crop phenology monitoring, such as field observations, can be prone to error, labour-intensive, and inefficient, particularly for crops with rapid growth and not very defined phenophases, such as vegetable crops. Thus, developing an accurate phenology monitoring system for vegetable crops is an important step towards sustainable practices. This paper evaluates the ability of computer vision (CV) techniques coupled with deep learning (DL) (CV_DL) as tools for the dynamic phenological classification of multiple vegetable crops at the subfield level, i.e., within the plot. Three DL models from the Single Shot Multibox Detector (SSD) architecture (SSD Inception v2, SSD MobileNet v2, and SSD ResNet 50) and one from You Only Look Once (YOLO) architecture (YOLO v4) were benchmarked through a custom dataset containing images of eight vegetable crops between emergence and harvest. The proposed benchmark includes the individual pairing of each model with the images of each crop. On average, YOLO v4 performed better than the SSD models, reaching an F1-Score of 85.5%, a mean average precision of 79.9%, and a balanced accuracy of 87.0%. In addition, YOLO v4 was tested with all available data approaching a real mixed cropping system. Hence, the same model can classify multiple vegetable crops across the growing season, allowing the accurate mapping of phenological dynamics. This study is the first to evaluate the potential of CV_DL for vegetable crops' phenological research, a pivotal step towards automating decision support systems for precision horticulture.

2023

Effects of Exogenously Applied Copper in Tomato Plants' Oxidative and Nitrogen Metabolisms under Organic Farming Conditions

Authors
Alves, A; Ribeiro, R; Azenha, M; Cunha, M; Teixeira, J;

Publication
HORTICULTURAE

Abstract
Currently, copper is approved as an active substance among plant protection products and is considered effective against more than 50 different diseases in different crops, conventional and organic. Tomato has been cultivated for centuries, but many fungal diseases still affect it, making it necessary to control them through antifungal agents, such as copper, making it the primary form of fungal control in organic farming systems (OFS). The objective of this work was to determine whether exogenous copper applications can affect AOX mechanisms and nitrogen use efficiency in tomato plant grown in OFS. For this purpose, plants were sprayed with 'Bordeaux' mixture (SP). In addition, two sets of plants were each treated with 8 mg/L copper in the root substrate (S). Subsequently, one of these groups was also sprayed with a solution of 'Bordeaux' mixture (SSP). Leaves and roots were used to determine NR, GS and GDH activities, as well as proline, H2O2 and AsA levels. The data gathered show that even small amounts of copper in the rhizosphere and copper spraying can lead to stress responses in tomato, with increases in total ascorbate of up to 70% and a decrease in GS activity down to 49%, suggesting that excess copper application could be potentially harmful in horticultural production by OFS.

Supervised
thesis

2022

Artificial Intelligence and Computer Vision as a Cognitive Tool for Precision Agriculture Applications

Author
Germano Filipe da Silva Moreira

Institution
UP-FEUP

2022

Assessing the stress effects of exogenously applied copper in tomato plant grown in organic farming

Author
Alexandre da Silva Alves

Institution
UP-FCUP

2022

Digital Twins for Precision Agriculture - Assimilation of Digital Phenotyping. Robotics and Artificial Intelligence

Author
Leandro de Almeida Rodrigues

Institution
UP-FCUP

2022

Early detection and identification of plant diseases caused by bacteria based on proximal sensingfrom a precision agriculture perspective

Author
Mafalda Alexandra Reis Pereira

Institution
UP-FCUP

2022

Advanced methodologies for the diagnosis of agronomic processes based on systems biology forprecision agriculture

Author
Renan Tosin

Institution
UP-FCUP