This thesis presents an investigation on innovative approaches to agricultural management, addressing challenges in both viticulture and turfgrass management. The first topic of this thesis introduces the Adaptive Crop Load Estimation (ACLE) method, a deep learning-based grape counting approach designed to alleviate the need for extensive annotated datasets. By training the model on a limited set of images, this method demonstrates promising results in accurately estimating grape cluster counts across different zones in the vineyards, with an average Mean Absolute Error (MAE)/Root Mean Square Error (RMSE) of 0.86/0.66. The ACLE method aims to reduce the cost of deploying automated grape counting systems by minimizing manual image annotation efforts and enabling model reusability across different vineyards. The second topic of this thesis delves into the realm of Turfgrass management, recognizing its pivotal roles in environmental health and aesthetics. Focusing on the challenges posed by spot- based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This framework employs Unmanned Ground Vehicles (UGV) for targeted spot spraying, optimizing robot stops and trajectories based on varying scenarios such as different spot sizes and robot capabilities. The trajectory planner developed within STPAS utilizes GPS coordinates and the radius of affected areas to determine efficient stops and paths for autonomous vehicles. Comparative analysis on the developed simulators reveals that STPAS reduces the distance traveled and time taken for spot spraying by over 50% compared to conventional boom-based sprayers, thereby enhancing both economic and environmental sustainability in Turfgrass management practices. / Master of Science / This thesis explores solutions for improving agricultural practices, specifically focusing on grapevine cultivation and turfgrass management. The first part introduces a novel method called Adaptive Crop Load Estimation (ACLE), which employs deep learning to accurately count grape clusters in vineyards. Unlike traditional methods requiring extensive annotated data, ACLE demonstrates significant results with minimal training images, aiming to reduce the cost of automated grape counting systems and enhance their adaptability across various vineyards. In the second part, the thesis delves into development of planning algorithm for precision spot spraying. Addressing challenges posed by spot-based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This algorithm provides robot stops and optimizes routes based on different scenarios such as spot sizes and robot capabilities. Comparative analysis of the simulation results reveals that STPAS improves efficiency, reducing both the distance traveled and time taken for spot spraying compared to boom-based sprayers. This not only benefits economic considerations but also contributes to environmental sustainability in turfgrass management practices.

Dans le cadre de la pulvérisation agricole, la diminution de la quantité des intrants est devenue une étape cruciale, et ce notamment en viticulture. La pulvérisation de précision en viticulture implique cependant une maitrise conjointe du matériel utilisé, des produits et de la répartition de ces produits sur le feuillage. Dans ce contexte, nombreuses sont les recherches menées sur l’optimisation d’utilisation des produits phytosanitaires, leur objectif final étant de réduire de manière significative la quantité d’intrants dans la culture. Cependant, peu de travaux ont été effectués sur l’étude du comportement des produits directement sur le feuillage, ce qui constitue donc l’objectif de cette thèse. La première partie de ce travail est consacrée particulièrement à l’analyse de l’état de la surface foliaire, en se focalisant spécifiquement sur l’étude de la rugosité de surface de la feuille qui constitue un paramètre essentiel dans le processus d’adhésion du produit pulvérisé sur la feuille. L’analyse de la surface de la feuille est réalisée en déterminant les caractéristiques texturales extraites d’images microscopiques. Un nouvel indicateur de rugosité est proposé ainsi que, des paramètres spatiaux et fréquentiels sont utilisés pour estimer et la rugosité de la feuille. Ces paramètres permettent ensuite la caractérisation de l’homogénéité de la surface et la détection des nervures/poils au niveau de la surface de la feuille. Cette partie représente une base fondamentale pour mieux comprendre le comportement des gouttelettes pulvérisées sur la feuille de vigne. La deuxième partie de ce travail de thèse est consacrée à des études expérimentales, qui ont pour but de définir et construire des modèles statistiques permettant d’estimer la quantité de produit restant sur la feuille ou la surface occupée par les gouttes. Ces modèles prennent en considération différents paramètres de pulvérisation, tels que la taille de la goutte et sa vitesse, la tension superficielle du produit, l’angle d’inclinaison et la rugosité de la surface de la feuille. Ces modèlespourraient être vus comme des outils de décision communs pour optimiser la quantité du produit pulvérisé et l’estimation du produit restant sur la feuille, et comme un outil d’aide pour optimiser les bancs d’essais et de tests de la qualité de la pulvérisation. / In the context of agricultural spraying, reducing the amount of input became a crucial step particularly in viticulture. The development of spraying precision in this domain needs the mastery of the use of spray equipment, product and distribution of these products on the foliage. In this area, many research have been done, their main goal being to optimize the use of plant product protection and to reduce significantly the input quantity inside the culture. However, few research has been done on the behavior of the product directly on the foliage which constitutes finally the main goal of this thesis. The first part of this report deals particularly with the analysis of leaf surface state by focusing precisely on the leaf surface roughness, one of the main parameters in product adhesion process. A leaf surface analysis is performed by determining the textural features extracted from microscopic images. A new roughness indicator is proposed and, spatial and frequency parameters were used to estimate and characterize the leaf roughness. These parameters allow both the characterization of surface homogeneity and the detection of the presence of rib/hair on the leaf surface. Indeed, this part represents a fundamental basis for understanding the spray droplet behavior on the vine leaf. The second part of this thesis deals with experimental studies which aim to define and to create statistical models to estimate the amount of product remaining on the leaf surface or the surface occupied by droplets. These models consider different spray parameters, such as droplet size and velocity, surface tension of the product, slope angle and roughness of the leaf. These models could be seen as aid-decision tools to optimize the amount of spray and the estimated product remaining on the leaf.