Grâce aux informations spatiales et spectrales qu'elle apporte, l'imagerie multispectrale de la peau est devenue un outil incontournable de la dermatologie. Cette thèse a pour objectif d'évaluer l’intérêt de cet outil pour la cosmétologie à travers trois études : la détection d'un fond de teint, l'évaluation de l'âge et la mesure de la rugosité.Une base d'images multispectrales de peau est construite à l'aide d'un système à multiples filtres optiques. Une phase de prétraitement est nécessaire à la standardisation et à la mise en valeur de la texture des images.Les matrices de covariance des acquisitions peuvent être représentées dans un espace multidimensionnel, ce qui constitue une nouvelle approche de visualisation de données multivariées. De même, une nouvelle alternative de réduction de la dimensionnalité basée sur l'ACP est proposée dans cette thèse. L'analyse approfondie de la texture des images multispectrales est réalisée : les paramètres de texture issus de la morphologie mathématique et plus généralement de l'analyse d'images sont adaptés aux images multivariées. Dans cette adaptation, plusieurs distances spectrales sont expérimentées, dont une distance intégrant le modèle LIP et la métrique d'Asplünd.Les résultats des prédictions statistiques générées à partir des données de texture permettent de conclure quant à la pertinence du traitement des données et de l'utilisation de l'imagerie multispectrale pour les trois études considérées. / Thanks to its precision in spatial and spectral domain, multispectral imaging has become an essential tool in dermatology. This thesis focuses on the interest of this technology for cosmetological parameters assessment through three different studies: the detection of a foundation make-up, age assessment and roughness measurement.A database of multispectral skin images is build using a multiple optical filters system. A preprocessing step allows to standardize those texture images before their exploitation.Covariance matrices of mutispectral acquisitions can be displayed in a multidimensional scaling space which is a novel way to represent multivariate data sets. Likewise, a new dimensionality reduction algorithm based on PCA is proposed in this thesis.A complete study of the images texture is performed: texture features from mathematical morphology and more generally from image analysis are expanded to the case of multivariate images. In this process, several spectral distances are tested, among which a new distance associating the LIP model to the Asplund metric.Statistical predictions are generated from texture data. Thoses predictions lead to a conclusion about the data processing efficiency and the relevance of multispectral imaging for the three cosmetologic studies.

L’évaluation visuelle de lésions cutanées est l’analyse la plus couramment réalisée par les dermatologues. Ce diagnostic s’effectue principalement à l’œil nu et se base sur des critères tels que la taille, la forme, la symétrie mais principalement la couleur. Cependant, cette analyse est subjective car dépendante de l’expérience du praticien et des conditions d’utilisation. Nous proposons dans ce manuscrit (1) le développement d’une caméra multispectrale spécialement conçue pour un usage en dermatologie. Cette caméra multispectrale se base sur la technologie de roue porte-filtres composée de filtres interférentiels et d’un algorithme basé sur les réseaux de neurones générant un cube hyperspectral de données cutanées. Cet ensemble combine l’avantage d’un spectrophotomètre (information spectrale), et celui d’une caméra (information spatiale). Son intérêt est également de délivrer une information reproductible et indépendante des conditions d’acquisition. La mise en place d’un protocole d’acquisition de données de peaux saines issues de cinq des six phototypes existants a permis la validation de notre système en comparant les spectres générés par notre système avec des spectres théoriques acquis par un spectrophotomètre professionnel. (2) La réflectance spectrale de données de peau fournit une information précieuse, car directement liée à sa composition en chromophores. La mesure quantitative des propriétés optiques du tissu cutané peut être basée sur la modélisation de la propagation de la lumière dans la peau. Pour cela, nous nous sommes appuyés sur le modèle de Kubelka-Munk, auquel nous avons associé une méthode d’optimisation basée sur les algorithmes évolutionnaires. Cette dernière apporte une réponse à l’inversion de ce modèle. A partir de cette approche, la quantification de divers paramètres de la peau peut être obtenue, tels que la mélanine et l’hémoglobine. (3) La validation de cette méthodologie est effectuée sur des données pathologiques (vitiligo et melasma) et permet de quantifier une différence de composition entre zone saine et zone affectée sur une même image. / Visual evaluation of cutaneous lesions is the analysis the most commonly performedby dermatologists. This diagnostic is mainly done by naked eye and is based on criterionsuch as the size, shape, symmetry but principally on colour of the lesions. However, thisanalysis is subjective because it depends on the practician experience and the acquisitionconditions. We propose in this dissertation (1) the development of a multispectralcamera specifically dedicated for dermatological use. This device is based on a filterwheel composed of interferential filters and a neural network-based algorithm, generatinga hyperspectral cube of cutaneous data. This setting combines advantage of both spectrophotometer(spectral information) and digital camera (spatial information). Its maininterest is also to provide reproducible information which is independent of the acquisitionconditions. The setting-up of an acquisition protocol of healthy skin data from five of thesix exisiting skin phototypes allows the validation of our system by comparing spectragenerated by our system and theoretical spectra acquired by professional spectrophotometer.(2) Skin spectral reflectance provides precious information because it is directly linkedto the skin chromophore composition. Quantitative measure of cutaneous tissue opticalproperties can be based on the modelisation of light propagation in skin. For this purpose,we based our method on Kubelka-Munk model with which we associated an optimizationmethod based on evolutionary algorithm. This method helps for the model inversion.Using this approach, quantification of diverse parameters of skin can be obtained such asmelanin and haemoglobin. (3) The validation of this model is performed on disease skindata (vitiligo and melasma) and allows to quantify difference between healthy and affectedskin area within a single image.

Microalgae cultivation has received much research attention in recent decades due to its high photosynthetic productivity and ability to produce biofuel feedstocks as well as high value compounds for the health food, cosmetics, and agriculture markets. Microalgae are conventionally grown in open pond raceways or closed photobioreactors. Due to the high water contents of these cultivation systems, they require large energy inputs for pumping and mixing the dilute culture, as well as concentrating and dewatering the resultant biomass. The energy required to operate these systems is generally greater than the energy contained in the resultant biomass, which precludes their use in sustainable biofuel production. To address this challenge, we designed a novel photobioreactor inspired by higher plants. In this synthetic leaf system, a modified transpiration mechanism is used which delivers water and nutrients to photosynthetic cells that grow as a biofilm on a porous, wicking substrate. Nutrient medium flow through the reactor is driven by evaporation, thereby eliminating the need for a pump. This dissertation outlines the design, construction, operation, and modeling of such a synthetic leaf system for energy positive biofuel production. First, a scaled down synthetic leaf reactor was operated alongside a conventional stirred tank photobioreactor. It was demonstrated that the synthetic leaf system required only 4% the working water volume as the conventional reactor, and showed growth rates as high as four times that of the conventional reactor. However, inefficiencies in the synthetic leaf system were identified and attributed to light and nutrient limitation of growth in the biofilm. To address these issues, a modeling study was performed with the aim of balancing the fluxes of photons and nutrients in the synthetic leaf environment. The vascular nutrient medium transport system was also modeled, enabling calculation of nutrient delivery rates as a function of environmental parameters and material properties of the porous membrane. These models were validated using an experimental setup in which the nutrient delivery rate, growth rate, and photosynthetic yield were measured for single synthetic leaves. The synthetic leaf system was shown to be competitive with existing technologies in terms of biomass productivity, while requiring zero energy for nutrient and gas delivery to the microorganisms. Future studies should focus on utilizing the synthetic leaf system for passive harvesting of secreted products in addition to passive nutrient delivery. / text

Σκοπός της διπλωματικής είναι η κατασκευή συστήματος που θα δημιουργεί φασματική απεικόνιση αντικειμένων χρησιμοποιώντας μια απλή ψηφιακή φωτογραφική μηχανή και φίλτρα τα οποία θα εναλλάσσονται μπροστά στον φακό της μηχανής με την βοήθεια βηματικού κινητήρα. / The aim of the project is the construction of a system that creates spectral imaging objects using a simple digital camera and filters which rotates in front of the camera lens by means stepper motor.

La gradation de lames de biopsie fournit des informations pronostiques essentielles pour le diagnostic et le traitement. La détection et le comptage manuel des mitoses est un travail fastidieux, sujet à des variations inter-et intra- observateur considérables. L'objectif principal de cette thèse de doctorat est le développement d'un système capable de fournir une détection des mitoses sur des images provenant de différents types de scanners rapides automatiques, ainsi que d'un microscope multispectral. L'évaluation des différents systèmes proposés est effectuée dans le cadre du projet MICO (MIcroscopie COgnitive, projet ANR TecSan piloté par notre équipe). Dans ce contexte, les systèmes proposés ont été testés sur les données du benchmark MITOS. En ce qui concerne les images couleur, notre système s'est ainsi classé en deuxième position de ce concours international, selon la valeur du critère F-mesure. Par ailleurs, notre système de détection de mitoses sur images multispectrales surpasse largement les meilleurs résultats obtenus durant le concours. / Digital pathology represents one of the major and challenging evolutions in modernmedicine. Pathological exams constitute not only the gold standard in most of medicalprotocols, but also play a critical and legal role in the diagnosis process. Diagnosing adisease after manually analyzing numerous biopsy slides represents a labor-intensive workfor pathologists. Thanks to the recent advances in digital histopathology, the recognitionof histological tissue patterns in a high-content Whole Slide Image (WSI) has the potentialto provide valuable assistance to the pathologist in his daily practice. Histopathologicalclassification and grading of biopsy samples provide valuable prognostic information thatcould be used for diagnosis and treatment support. Nottingham grading system is thestandard for breast cancer grading. It combines three criteria, namely tubule formation(also referenced as glandular architecture), nuclear atypia and mitosis count. Manualdetection and counting of mitosis is tedious and subject to considerable inter- and intrareadervariations. The main goal of this dissertation is the development of a framework ableto provide detection of mitosis on different types of scanners and multispectral microscope.The main contributions of this work are eight fold. First, we present a comprehensivereview on state-of-the-art methodologies in nuclei detection, segmentation and classificationrestricted to two widely available types of image modalities: H&E (HematoxylinEosin) and IHC (Immunohistochemical). Second, we analyse the statistical and morphologicalinformation concerning mitotic cells on different color channels of various colormodels that improve the mitosis detection in color datasets (Aperio and Hamamatsu scanners).Third, we study oversampling methods to increase the number of instances of theminority class (mitosis) by interpolating between several minority class examples that lietogether, which make classification more robust. Fourth, we propose three different methodsfor spectral bands selection including relative spectral absorption of different tissuecomponents, spectral absorption of H&E stains and mRMR (minimum Redundancy MaximumRelevance) technique. Fifth, we compute multispectral spatial features containingpixel, texture and morphological information on selected spectral bands, which leveragediscriminant information for mitosis classification on multispectral dataset. Sixth, we performa comprehensive study on region and patch based features for mitosis classification.Seven, we perform an extensive investigation of classifiers and inference of the best one formitosis classification. Eight, we propose an efficient and generic strategy to explore largeimages like WSI by combining computational geometry tools with a local signal measureof relevance in a dynamic sampling framework.The evaluation of these frameworks is done in MICO (COgnitive MIcroscopy, ANRTecSan project) platform prototyping initiative. We thus tested our proposed frameworks on MITOS international contest dataset initiated by this project. For the color framework,we manage to rank second during the contest. Furthermore, our multispectral frameworkoutperforms significantly the top methods presented during the contest. Finally, ourframeworks allow us reaching the same level of accuracy in mitosis detection on brightlightas multispectral datasets, a promising result on the way to clinical evaluation and routine.

Ce travail de thèse porte sur l'imagerie multispectrale à haut contraste pour la détection et la caractérisation directe d'exoplanètes. Dans ce contexte, le développement de méthodes innovantes de traitement d'images est indispensable afin d'éliminer les tavelures quasi-statiques dans l'image finale qui restent à ce jour, la principale limitation pour le haut contraste. Bien que les aberrations résiduelles instrumentales soient à l'origine de ces tavelures, aucune méthode de réduction de données n'utilise de modèle de formation d'image coronographique qui prend ces aberrations comme paramètres. L'approche adoptée dans cette thèse comprend le développement, dans un cadre bayésien, d'une méthode d'inversion fondée sur un modèle analytique d'imagerie coronographique. Cette méthode estime conjointement les aberrations instrumentales et l'objet d'intérêt, à savoir les exoplanètes, afin de séparer correctement ces deux contributions. L'étape d'estimation des aberrations à partir des images plan focal (ou phase retrieval en anglais), est la plus difficile car le modèle de réponse instrumentale sur l'axe dont elle dépend est fortement non-linéaire. Le développement et l'étude d'un modèle approché d'imagerie coronographique plus simple se sont donc révélés très utiles pour la compréhension du problème et m'ont inspiré des stratégies de minimisation. J'ai finalement pu tester ma méthode et d'estimer ses performances en terme de robustesse et de détection d'exoplanètes. Pour cela, je l'ai appliquée sur des images simulées et j'ai notamment étudié l'effet des différents paramètres du modèle d'imagerie utilisé. J'ai ainsi démontré que cette nouvelle méthode, associée à un schéma d'optimisation fondé sur une bonne connaissance du problème, peut fonctionner de manière relativement robuste, en dépit des difficultés de l'étape de phase retrieval. En particulier, elle permet de détecter des exoplanètes dans le cas d'images simulées avec un niveau de détection conforme à l'objectif de l'instrument SPHERE. Ce travail débouche sur de nombreuses perspectives dont celle de démontrer l'utilité de cette méthode sur des images simulées avec des coronographes plus réalistes et sur des images réelles de l'instrument SPHERE. De plus, l'extension de la méthode pour la caractérisation des exoplanètes est relativement aisée, tout comme son extension à l'étude d'objets plus étendus tels que les disques circumstellaire. Enfin, les résultats de ces études apporteront des enseignements importants pour le développement des futurs instruments. En particulier, les Extremely Large Telescopes soulèvent d'ores et déjà des défis techniques pour la nouvelle génération d'imageurs de planètes. Ces challenges pourront très probablement être relevés en partie grâce à des méthodes de traitement d'image fondées sur un modèle direct d'imagerie. / This research focuses on high contrast multispectral imaging in the view of directly detecting and characterizing Exoplanets. In this framework, the development of innovative image post-processing methods is essential in order to eliminate the quasi-static speckles in the final image, which remain the main limitation for high contrast. Even though the residual instrumental aberrations are responsible for these speckles, no post-processing method currently uses a model of coronagraphic imaging, which takes these aberrations as parameters. The research approach adopted includes the development of a method, in a Bayesian Framework, based on an analytical coronagraphic imaging model and an inversion algorithm, to estimate jointly the instrumental aberrations and the object of interest, i.e. the exoplanets, in order to separate properly these two contributions. The instrumental aberration estimation directly from focal plane images, also named phase retrieval, is the most difficult step because the model of on-axis instrumental response, of which these aberrations depend on, is highly non-linear. The development and the study of an approximate model of coronagraphic imaging thus proved very useful to understand the problem at hand and inspired me some minimization strategies. I finally tested my method and estimated its performances in terms of robustness and exoplanets detection. For this, I applied it to simulated images and I studied the effect of the different parameters of the imaging model I used. The findings from this research provide evidence that this method, in association with an optimization scheme based on a good knowledge of the problem at hand, can operate in a relatively robust way, despite the difficulties of the phase retrieval step. In particular, it allows the detection of exoplanets in the case of simulated images with a detection level compliant with the goal of the SPHERE instrument. The next steps will be to verify the efficiency of this new method on simulated images using more realistic coronagraphs and on real images from the SPHERE instrument. In addition, the extension of the method for the characterization of exoplanets is relatively easy, as its extension to the study of larger objects such as circumstellar disks. Finally, the results of this work will also bring some crucial insights for the development of future instruments. In particular, the Extremely Large Telescopes have already risen some technical challenges for the next generation of planet finders, which may partly be addressed by an image processing method based on an imaging model.

In the previous years, 3D imaging has found his way into the world of Egyptology. This lecture will present two case studies where 3D technology is used for the documentation of hieratic inscriptions. The inscriptions, painted in (red) ochre or black paint, were applied on different carriers, and required a different methodology. The Egyptian collection of the Royal Museums of Art and History (RMAH Brussels) contains a large number of small decorated and/or inscribed objects. Some of these objects are currently in a bad condition - any operation carried on them can result in considerable material losses -, making it necessary to document them in such a way that it allows future scholars to study them in detail without handling them. The EES Project therefore aims to create multispectral 3D images of these fragile objects with a multispectral ‘minidome’ acquisition system, based on the already existing system of the multi-light Portable Light Dome (PLD). The texture/colour values on the created 2D+ and 3D models are interactive data based on a recording process with infrared, red, green, blue, and ultraviolet light spectra. Software tools and enhancement filters have been developed which can deal with the different wavelengths in real-time. This leads to an easy and cost-effective methodology which combines multispectral imaging with the actual relief characteristics and properties of the physical object. The system is transportable to any collection or excavation in the field. As a case study, the well-known Brussels “Execration Figurines” (Middle Kingdom, c. 1900 BC) were chosen. These figurines are made of unbaked clay and covered with hieratic texts, listing names of foreign countries and rulers. The study of this type of collections is mostly hampered by the poor state of conservation of the objects, but also by the only partial preservation of the ink traces in visible light. The method has also been applied to other decorated objects of the RMAH collection, such as a Fayoum portrait, ostraca and decorated objects made of stone, wood and ceramics. The final goal will be to publish the newly created multispectral 3D images on Carmentis (www.carmentis.be), the online catalogue of the RMAH collection, making them accessible to scholars all over the world. The second case study presents the quarry inscriptions of the New Kingdom limestone quarries at Dayr Abu Hinnis (Middle Egypt). These gallery quarries contain hundreds of hieratic inscriptions, written on the ceiling. The texts are mainly related to the general administration of the quarry area. In documenting the abundance of ceiling inscriptions and other graffiti, we had to decide upon a practice that would allow not only to capture the \"content\", but also to document the location and orientation of each record. Every inscription can be photographed in detail, but this is insufficient to provide the reader access to vital information concerning the spatial distribution of the inscriptions, which may, for instance, relate to the progress of work. After experimenting with a variety of other methods, we adopted a photogrammetric software for 3D modelling photographs of the quarry ceilings, AGISOFT PHOTOSCAN, which uses structure from motion (SFM) algorithms to create three-dimensional images based on a series of overlapping two-dimensional images. The ultimate goal of this whole labour-intensive process in the quarries is not the creation of pure threedimensional models, but rather to generate an orthophoto of the entire ceiling of a quarry. Based on these images, each graffito could be analysed in context.

Cloud detection on satellite imagery is an essential pre-processing step for several remote sensing applications. In general, machine learning based methods for cloud detection perform well, especially the ones based on deep learning as they consider both spatial and spectral features of the input image. However, false alarms become a major issue in winter images, wherein bright objects like snow/ice are also detected as cloud. This affects further image analysis like urban change detection, weather forecast, disaster risk management. In this thesis, we consider optical remote sensing images from small satellites constellation of PlanetScope. These have limited multispectral capacity of four bands: Red, Green, Blue (RGB) and Near-Infrared (NIR) bands. Detection algorithms tend to be more efficient when considering information from more than one spectral band to perform the detection. This study explores the data diversity provided by NIR band to RGB band images in terms of improvement in cloud detection accuracy. Two deep learning algorithms based on convolutional neural networks with different architectures are trained on RGB, NIR and RGB+NIR image data, resulting in six trained models. Each of these networks is tested with winter images of varying amounts of clouds and land covered with snow and ice. The evaluation is done based on performance metrics for accuracy and Intersection-over-Union (IoU) scores, as well as visual inspection. A total of eighteen experiments are performed, and it is observed that NIR band provides significant data diversity when combined with RGB bands, by reducing the false alarms and improving the accuracy. In terms of processing time, there is no significant increase for the algorithms evaluated, therefore better cloud detection can be achieved without significantly increasing the computational costs. Based on this analysis, Unibap iX10-100 embedded system is a possible choice for implementing these algorithms as it is suitable for AI applications. / Detektering av moln på satellitbilder är ett viktigt bearbetningssteg för flera fjärr analysapplikationer. I allmänhet fungerar maskininlärningsbaserade metoder för molndetektering bra, särskilt de som är baserade på djupinlärning eftersom de tar hänsyn till både spatiala och spektrala egenskaper i input bilder. Men falsklarm blir ett stort problem i vinterbilder, där medbringande föremål som snö/is också upptäcks som moln. Detta påverkar ytterligare bildanalyser som upptäckt av stadsförändringar, väderprognos, katastrofrisk-hantering. I denna avhandling tar vi hänsyn till optiska fjärranalysbilder från små satellitkonstellationer PlanetScope. Dessa har begränsad multispektral kapacitet på fyra band: röda, gröna, blå (RGB) och near-infrared (NIR) band. Detektionsalgoritmer tenderar att vara mer effektiva när man överväger information från mer än ett spektralband för att utföra detekteringen. Denna studie utforskar datadiversiteten som tillhandahålls av NIR-band till RGB-bandbilder när det gäller förbättring av molndetekteringsnoggrannheten. Två djupinlärningsalgoritmer baserade på konvolutionella neurala nätverk med olika arkitekturer tränas på RGB-, NIR- och RGB+NIR-bilddata, vilket resulterar i sex tränade modeller. Vart och ett av dessa nätverk testas med vinterbilder av varierande mängder moln och land täckt med snö och is. Utvärderingen görs baserat på prestandamått för noggrannhet och Intersection-over-Union (IoU) poäng, samt visuell inspektion. Totalt arton experiment utförs, och det observeras att NIR-bandet ger betydande datadiversitet när det kombineras med RGB-band, genom att minska de falska larmen och förbättra noggrannheten. När det gäller bearbetningstid finns det ingen signifikant ökning av den för de utvärderade algoritmerna, därför kan bättre molndetektering uppnås utan att nämnvärt öka beräkningskostnaderna. Baserat på denna analys är Unibap iX10-100 inbyggt system ett möjligt val för implementera dessa algoritmer eftersom det är lämpligt för AI-tillämpningar.

In this dissertation, methods are designed and validated for the utilization of hyperspectral hypertemporal remotely sensed data in target detection applications. Two new classes of methods are designed to optimize the selection of target detection features from spectro-temporal space data. The first method is based on the consideration that all the elements of the spectro-temporal map are independent of each other. The second method is based on the consideration that the elements of the spectro-temporal map have some vicinal dependency among them. Methods designed for these two approaches include various stepwise selection methods, windowing approaches, and clustering techniques. These techniques are compared to more traditional feature extraction methods such as Normalized Difference Vegetation Index (NDVI), spectral analysis, and Principal Component Analysis (PCA). The efficacies of the new methods are demonstrated within an aquatic invasive species detection application, namely discriminating waterhyacinth from other aquatic vegetation such as American lotus. These two aquatic plant species are chosen for testing the proposed methods as they have very similar physical characteristics and they represent a practical life target detection problem. It is observed from the overall classification accuracy estimates that the proposed feature extraction methods show a marked improvement over conventional methods. Along with improving the accuracy estimates, these methods demonstrate a capability to drastically reduce the dimensionality while retaining the desired hyperspectral hypertemporal features. Furthermore, the feature set extracted using the newly developed methods provide information about the optimum subset of the hyperspectral hypertemporal data for a specific target detection application, which makes these methods serve as tools to strategize more intelligent data collection plans.

Potatoes in the Eastern Shore of Virginia are traditionally planted between late February and early April and harvested between early June and late August. Potato prices are usually higher early into the harvest season and decrease slowly as the season progresses. Early planting dates are desirable for farmers, as it allows them to perceive higher prices for their product, but early planting is also associated with lower air temperature during the early season, which in turn can affect plant development, water and nutrient uptake, and overall yield. Additionally, variations in soil properties often affect nutrient and water availability for plants, as well as the distribution of soil-borne insect pests. Additionally, several techniques are available to map the variations of soil properties in commercial potato fields, but little effort has been made to relate this information to the potential presence of soil-borne pests. Hence, the objective of this project was to evaluate the effect of planting dates, nitrogen (N) rates, and irrigation regimes on potato production. Two comprehensive studies were conducted between February and July 2022 and 2023. The objective of the first study was to evaluate the effect of N rates, planting dates, and soil physicochemical properties in potato production and the presence of soil-borne pests. This study was established in a split-plot design with four replications, with planting dates on the main plot and N rates and time of application on the sub-plot. Late March planting resulted in the highest total tuber yield, while early planting produced significantly larger tubers. Early March planting reduced plant development and emergence, probably due to lower air and soil temperatures. There was no interaction between planting dates and N applications. Using N rates higher than 147 kg ha-1 resulted in no significant differences in total tuber yield. Regression analyses showed that the Normalized Differences Red Edge (NDRE) is an excellent predictor of N content in plant tissue and tuber yield. Moreover, Ca and H saturation percentages were linked to wireworm damage levels using classification algorithms. Similarly, K saturation percentage was identified as a potential predictor of nematode presence in this region. A second study was established with the objective of evaluating the effect of N rates and irrigation regimes on potato production. The study was established in a split-plot design with four replications, with the irrigation method on the main plot and total N rate on the subplot. Results from these experiments showed higher growth and tuber yield when combining overhead irrigation with crop evapotranspiration (ETc) estimation. Moreover, there were no significant differences when using N rates higher than 112 kg ha-1. Overall, results from these experiments suggest no changes in current N rate recommendations for this region. Additionally, these results suggest planting in late March and using irrigation regimes based on evapotranspiration with overhead irrigation systems. Future research should focus on adaptive fertilization based on growing degree days and refinement irrigation determination practices. / Master of Science in Life Sciences / In the Eastern Shore of Virginia, nearly 4,000 acres are annually dedicated to fresh white potato farming. The established planting window extends from early March to early April, aligned with peak market demands in late April. However, this traditional planting strategy exposes crops to varying temperatures, potentially affecting water and nutrient demands, as well as overall yield. A research project consisting of two studies was conducted with the objective of evaluating the effect of planting dates, nitrogen (N) rates, and irrigation regimes on potato production. The first study was conducted with the aim of optimizing yield and nutrient management by exploring the interplay between planting dates, N rates, and application timing. The second study evaluated overhead and subsurface drip irrigation systems with irrigation regimes determined either by crop evapotranspiration (ETc) or by soil moisture content through soil water sensors (SWS). Results demonstrated that early March planting resulted in delayed emergence and overall growth due to colder temperatures, while late March plantings produced the highest tuber yields. On the irrigation front, overhead irrigation integrated with ETc estimation consistently improved plant health and augmented yield. In addition, the Normalized Differences Red Edge (NDRE) index, obtained from multispectral drone imaging, produced a significant correlation with N content in plant tissue and with total tuber yields for both studies. This suggests its high potential as a yield prediction tool. Overall, results from these studies reinforce current N rate recommendations for Virginia. Furthermore, they not only refine regional potato cultivation practices but also suggest the need for research pivoting around adaptive fertilization based on growing degree days and the potential refinement of irrigation regimens.