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Coffee is one of the most consumed agricultural products in the world, representing a vast market and, for many, a true ritual. Its cultivation, on which many farming families depend, is sometimes threatened by a fungus: Hemileia vastatrix, which spreads a disease with devastating consequences: Coffee Leaf Rust (CLR).

The severity of recent epidemics has highlighted the necessity of adapting agricultural techniques to prevent this disease.

Among these techniques, agroforestry has an impact on a factor influencing the spread of rust: the precipitation received by coffee plants.

This thesis examines the mechanical response of coffee leaves to raindrop impacts and, in particular, how this response can lead to spore detachment.

Through a series of experiments conducted at the Microfluidics Lab at the University of Liège, the thesis explores the deformation modes of coffee leaves, such as bending, twisting, and flapping, following the impact of water droplets. High-speed videography, videogrammetry, and numerical methods were employed, allowing for the analysis of positions, velocities, and accelerations.

The main findings include the identification of flapping as the movement most likely to cause spore detachment, the quantification of leaf accelerations under various conditions, and the influence of different parameters related to the leaf or the droplet.

The study concludes with recommendations for future research on leaf flapping and its influence on spore propagation. Le café est l'un des produits agricoles les plus consommés au monde, représentant un gigantesque marché et, pour beaucoup, un véritable rituel. Sa culture, sur laquelle repose un grand nombre de familles paysannes, est parfois menacée par un champignon : Hemileia vastatrix, qui propage une maladie aux conséquences fatales : la rouille du caféier (CLR).

La violence des dernières épidémies a mis en lumière la nécessité d'adapter les techniques agricoles pour prévenir cette maladie.

Parmi ces techniques, l'agroforesterie joue un rôle sur un facteur de propagation de la rouille : les précipitations reçues par les plants de café.

Cette thèse examine la réponse mécanique des feuilles de caféier aux impacts des gouttes de pluie, en particulier comment cette réponse peut provoquer le détachement de spores.

À travers une série d'expériences menées au Microfluidics Lab de l'Université de Liège, la thèse explore les modes de déformation des feuilles de caféier, tels que la flexion, la torsion et le battement, suite à l'impact des gouttes d'eau. Des techniques de vidéographie à haute vitesse, de vidéogrammétrie, des méthodes numériques et des régressions linéaires ont été employées, permettant une analyse des positions, vitesses et accélérations.

Les principales conclusions incluent l'identification du battement comme le mouvement le plus susceptible de provoquer le détachement des spores, la quantification des fréquences d'oscillation et des accélérations des feuilles dans diverses conditions, ainsi que l'analyse de l'influence de différents paramètres relatifs à la feuille ou à la goutte, notamment sa masse et sa vitesse d'impact.

L'étude se termine par des recommandations pour de futures recherches sur le battement des feuilles et son influence sur la propagation des spores.

Coffee Leaf Rust (CLR), Hemileia vastatrix, raindrop impact, mechanical response, leaf deformation, modal decomposition, bending, twisting, flapping, spore detachment, videogrammetry, high-speed videography, precipitation dynamics, disease propagation, agricultural practices --- Rouille du caféier, Hemileia vastatrix, impact des gouttes de pluie, réponse mécanique, déformation des feuilles, décomposition modale, flexion, torsion, battement, détachement des spores, vidéogrammétrie, vidéographie à haute vitesse, dynamique des précipitations, propagation des maladies, pratiques agricoles --- Ingénierie, informatique & technologie > Ingénierie mécanique

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This Special Issue explores underrepresented aspects of the political dimensions of global warming. It includes post- and decolonial perspectives on climate-related migration and conflict, intersectional approaches, and climate change politics as a new tool of governance. Its aim is to shed light on the social phenomena associated with anthropogenic climate change, as well as its multidimensional and far-reaching political effects, including climate-induced migration movements and climate-related conflicts in different parts of the world. In doing so, it critically engages with securitizing discourses and the resulting anti-migration arguments and policies in the Global North in order to identify and give a voice to alternative and hitherto underrepresented research and policy perspectives. In this way, it aims to contribute to a fact-based, critical, and holistic approach to human mobility and conflict in the context of political and environmental crisis.

telecoupling --- sustainability --- multi-stakeholder initiatives --- roundtable for sustainable palm oil --- sustainable natural rubber initiative --- climate change --- climigration --- environmental change --- migration --- mobility --- refugees --- relocation --- resettlement --- livelihoods --- Pacific Islands --- SIDS --- vulnerability --- exposure --- disasters --- violent conflict --- disaster risk reduction --- conflict prevention --- humanitarian assistance --- development assistance --- climate change migration --- adaptation --- displacement --- forced relocation --- forced migration --- Gilbertese people --- Phoenix Islands --- Wagina Island --- immobility --- environmental migration and mobility --- trapped populations --- migration governance --- Senegal --- Vietnam --- planned relocation --- migration-climate change-coffee nexus --- migration as adaptation --- in situ adaptation --- coffee leaf-rust --- transborder region --- narratives --- environmental migration --- environmental justice --- North–South relations --- climate change politics --- conflict --- intersectionality --- postcolonial studies

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill–Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching–learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- n/a --- Hill-Tsai failure criterion --- teaching-learning