A5036578505- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Horticultural and Viticultural Research
- Fermentation and Sensory Analysis
- Leaf Properties and Growth Measurement
- Spectroscopy and Chemometric Analyses
- Greenhouse Technology and Climate Control
- Medical Image Segmentation Techniques
- Plant responses to elevated CO2
- Video Coding and Compression Technologies
- CCD and CMOS Imaging Sensors
- Advanced Vision and Imaging
Universidade do Porto
2021-2024
INESC TEC
2023-2024
Institute for Systems Engineering and Computers
2023
The efficiency of agricultural practices depends on the timing their execution. Environmental conditions, such as rainfall, and crop-related traits, plant phenology, determine success irrigation. Moreover, seasonal biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, management conditions. Therefore, assessing crops’ phenological spatiotemporal variability can improve decision making, allowing thorough planning timely execution operations....
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialised labour is not available for proper classification or identification of grapevines, making the value commercial vines uncertain. Traditional methods such as genetic analysis ampelometry are time-consuming, expensive and often require expert skills that rarer. New vision-based systems benefit from advanced innovative technology can be used by non-experts in ampelometry. To this end, Deep...
The world society needs to produce more food with the highest quality standards feed population same level of nutrition. Microfarms and local production enable growing vegetables near reducing operational logistics costs related post-harvest handling. However, it isn't economical viable neither efficient have one person devoted these microfarms task. To overcome this issue, we propose an open-source robotic solution capable performing multitasks in small polyculture farms. This robot is...
This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations are presented via biochemical analyses hyperspectral data that then combined with systems biology techniques. In the study, conducted in ‘Moscatel Galego Branco’ vineyard Portugal’s Douro Wine Region during post-veraison, was applied predawn leaf water potential (Ψpd) measured...
Plant-soil sensing devices coupled with Artificial Intelligence autonomously collect and process in situ plant phenotypic data. A challenge of this approach is the limited incorporation phenotype data into decision support systems designed to harness agricultural practices forecast behavior within intricate context genotype, environment, management interactions (G × E M). To enhance role digital phenotyping supporting Precision Agriculture, paper proposes a network based on Internet Things....
Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, environmentally sustainable practices. The Soluble Solids Content (SSC) essential for assessing grape ripeness quality the winemaking process. Conventional methods determining SSC values (expressed °Brix) are invasive, expensive,...
Predawn leaf water potential (Ψpd) is the main parameter to determine plant status, and it has been broadly used support irrigation management. However, Scholander pressure chamber methodology laborious, time-consuming invasive. This study examined a low-cost hyperspectral proximal sensor estimate Ψpd in grapevine (Vitis vinifera L.). For this, both spectral reflectance (340–850 nm) were accessed grapevines commercial vineyard located Douro Wine Region, northeast Portugal. A machine-learning...