Etude et modélisation du comportement des gouttelettes de produits phytosanitaires sur les feuilles de vignes par imagerie ultra-rapide et analyse de texture

Dans le contexte actuel de diminution des pollutions d origine agricole, laréduction des apports d intrants devient un enjeu primordial. En France, laviticulture est l activité qui possède le taux le plus important de traitementsphytosanitaires par unité de surface. Elle représente, à elle seule, 20% de laconsommation annuelle de pesticides. Par conséquent, il est nécessaire d étudierle devenir des pesticides appliqués afin de réduire les quantités perduesdans l environnement. Dans le cadre de la réduction d apport de produitsphytosanitaires dans les vignes, de nombreux travaux ont été effectués sur lamodélisation du comportement d un spray de gouttelettes et sa répartitionau niveau de la parcelle et de l air environnant. Cependant, il est égalementimportant de s intéresser au comportement de la gouttelette directement auniveau de la feuille. Les progrès dans le domaine de l imagerie et la diminutiondu coût des systèmes ont rendus ces systèmes beaucoup plus attractifs.Le travail de cette thèse consiste en la mise en place d un système d imagerierapide qui permet l observation du comportement à l impact de gouttelettesrépondant aux conditions de pulvérisation. Les caractéristiques ainsi que lecomportement associé de chaque gouttelette sont extraits grâce à une méthodede suivi d objets. Une analyse statistique basée sur un nombre représentatifde résultats permet ensuite d évaluer de manière robuste le devenir d unegoutte en fonction de ses caractéristiques. Parallèlement, un paramètre décrivantl état de surface de la feuille est également étudié grâce à l imagerie : larugosité qui joue un rôle prédominant dans la compréhension des mécanismesd adhésionIn the domain of vineyard precision spraying research, one of the most importantobjectives is to minimize the volume of phytosanitary products ejected bya sprayer in order to be more environmentally respectful with more effectivevine leaf treatments. Unfortunaltely, even if lot of works have been carriedout at a parcel scale, mainly on losses caused by drift, less works have beencarried out at the leaf scale in order to understand which parameters influencethe spray quality. Since few years, recent improvements in image processing,sensitivity of imaging systems and cost reduction have increased the interestof high-speed imaging techniques. Analyzing the behavior of droplets afterimpact with the leaf thanks to high speed imaging technology is a relevantsolution. By this way, we propose a droplets behavior analyzing process invineyard spraying context based on high-speed acquision system combinedwith image processing techniques. This process allows us to extract dropletsparameters. Therefore, a statistical study is processed in order to determinethe effects of droplets parameters on leaf impact or to predict behavior of asingle droplet. Since this behavior is strongly related to leaf surface, we alsopropose to validate a natural leaf roughness characterization method basedon texture analysisDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

The Use of High-Speed Imaging Systems for Applications in Precision Agriculture

UB Dijon EcolDurInternational audienceThe book "New Technologies - Trends, Innovations and Research" presents contributions made by researchers from the entire world and from some modern fields of technology, serving as a valuable tool for scientists, researchers, graduate students and professionals. Some practical applications in particular areas are presented, offering the capability to solve problems resulted from economic needs and to perform specific functions. The book will make possible for scientists and engineers to get familiar with the ideas from researchers from some modern fields of activity. It will provide interesting examples of practical applications of knowledge, assist in the designing process, as well as bring changes to their research areas. A collection of techniques, that combine scientific resources, is provided to make necessary products with the desired quality criteria. Strong mathematical and scientific concepts were used in the applications. They meet the requirements of utility, usability and safety. Technological applications presented in the book have appropriate functions and they may be exploited with competitive advantages. The book has 17 chapters, covering the following subjects: manufacturing technologies, nanotechnologies, robotics, telecommunications, physics, dental medical technologies, smart homes, speech technologies, agriculture technologies and management

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of CNNs. For its validation, reproducibility and further comparison against other methods, we tested the method on a big multi-field expert annotated LGE-MRI database including healthy and diseased cases. Results and Conclusion: In an exhaustive comparison against nine reference algorithms, the proposal achieved state-of-the-art segmentation performances and showed to be the only method agreeing in volumetric scar quantification with the expert delineations. Moreover, the method was able to reproduce the intra- and inter-observer variability ranges. It is concluded that the method could suitably be transferred to clinical scenarios.Comment: Submitted to IEE

Study and modeling of the behavior of droplets of plant protection products on vine leaves by ultra-fast imaging and texture analysis

Dans le contexte actuel de diminution des pollutions d’origine agricole, laréduction des apports d’intrants devient un enjeu primordial. En France, laviticulture est l’activité qui possède le taux le plus important de traitementsphytosanitaires par unité de surface. Elle représente, à elle seule, 20% de laconsommation annuelle de pesticides. Par conséquent, il est nécessaire d’étudierle devenir des pesticides appliqués afin de réduire les quantités perduesdans l’environnement. Dans le cadre de la réduction d’apport de produitsphytosanitaires dans les vignes, de nombreux travaux ont été effectués sur lamodélisation du comportement d’un spray de gouttelettes et sa répartitionau niveau de la parcelle et de l’air environnant. Cependant, il est égalementimportant de s’intéresser au comportement de la gouttelette directement auniveau de la feuille. Les progrès dans le domaine de l’imagerie et la diminutiondu coût des systèmes ont rendus ces systèmes beaucoup plus attractifs.Le travail de cette thèse consiste en la mise en place d’un système d’imagerierapide qui permet l’observation du comportement à l’impact de gouttelettesrépondant aux conditions de pulvérisation. Les caractéristiques ainsi que lecomportement associé de chaque gouttelette sont extraits grâce à une méthodede suivi d’objets. Une analyse statistique basée sur un nombre représentatifde résultats permet ensuite d’évaluer de manière robuste le devenir d’unegoutte en fonction de ses caractéristiques. Parallèlement, un paramètre décrivantl’état de surface de la feuille est également étudié grâce à l’imagerie : larugosité qui joue un rôle prédominant dans la compréhension des mécanismesd’adhésionIn the domain of vineyard precision spraying research, one of the most importantobjectives is to minimize the volume of phytosanitary products ejected bya sprayer in order to be more environmentally respectful with more effectivevine leaf treatments. Unfortunaltely, even if lot of works have been carriedout at a parcel scale, mainly on losses caused by drift, less works have beencarried out at the leaf scale in order to understand which parameters influencethe spray quality. Since few years, recent improvements in image processing,sensitivity of imaging systems and cost reduction have increased the interestof high-speed imaging techniques. Analyzing the behavior of droplets afterimpact with the leaf thanks to high speed imaging technology is a relevantsolution. By this way, we propose a droplets behavior analyzing process invineyard spraying context based on high-speed acquision system combinedwith image processing techniques. This process allows us to extract dropletsparameters. Therefore, a statistical study is processed in order to determinethe effects of droplets parameters on leaf impact or to predict behavior of asingle droplet. Since this behavior is strongly related to leaf surface, we alsopropose to validate a natural leaf roughness characterization method basedon texture analysi

Etude et modélisation du comportement des gouttelettes de produits phytosanitaires sur les feuilles de vignes par imagerie ultra-rapide et analyse de texture

In the domain of vineyard precision spraying research, one of the most importantobjectives is to minimize the volume of phytosanitary products ejected bya sprayer in order to be more environmentally respectful with more effectivevine leaf treatments. Unfortunaltely, even if lot of works have been carriedout at a parcel scale, mainly on losses caused by drift, less works have beencarried out at the leaf scale in order to understand which parameters influencethe spray quality. Since few years, recent improvements in image processing,sensitivity of imaging systems and cost reduction have increased the interestof high-speed imaging techniques. Analyzing the behavior of droplets afterimpact with the leaf thanks to high speed imaging technology is a relevantsolution. By this way, we propose a droplets behavior analyzing process invineyard spraying context based on high-speed acquision system combinedwith image processing techniques. This process allows us to extract dropletsparameters. Therefore, a statistical study is processed in order to determinethe effects of droplets parameters on leaf impact or to predict behavior of asingle droplet. Since this behavior is strongly related to leaf surface, we alsopropose to validate a natural leaf roughness characterization method basedon texture analysisDans le contexte actuel de diminution des pollutions d’origine agricole, laréduction des apports d’intrants devient un enjeu primordial. En France, laviticulture est l’activité qui possède le taux le plus important de traitementsphytosanitaires par unité de surface. Elle représente, à elle seule, 20% de laconsommation annuelle de pesticides. Par conséquent, il est nécessaire d’étudierle devenir des pesticides appliqués afin de réduire les quantités perduesdans l’environnement. Dans le cadre de la réduction d’apport de produitsphytosanitaires dans les vignes, de nombreux travaux ont été effectués sur lamodélisation du comportement d’un spray de gouttelettes et sa répartitionau niveau de la parcelle et de l’air environnant. Cependant, il est égalementimportant de s’intéresser au comportement de la gouttelette directement auniveau de la feuille. Les progrès dans le domaine de l’imagerie et la diminutiondu coût des systèmes ont rendus ces systèmes beaucoup plus attractifs.Le travail de cette thèse consiste en la mise en place d’un système d’imagerierapide qui permet l’observation du comportement à l’impact de gouttelettesrépondant aux conditions de pulvérisation. Les caractéristiques ainsi que lecomportement associé de chaque gouttelette sont extraits grâce à une méthodede suivi d’objets. Une analyse statistique basée sur un nombre représentatifde résultats permet ensuite d’évaluer de manière robuste le devenir d’unegoutte en fonction de ses caractéristiques. Parallèlement, un paramètre décrivantl’état de surface de la feuille est également étudié grâce à l’imagerie : larugosité qui joue un rôle prédominant dans la compréhension des mécanismesd’adhésio

Automatic analyzis of droplet impact by high speed imaging

The impact of agricultural activities on the water quality is the consequence of the loss of fertilisers (chemical fertilisers, livestock effluent, also referred to as farm fertiliser, food-processing effluent and sludge) and crop treatment products (phytosanitary products). This pollution may prevent certain uses of water, notably its use for human and animal food (groundwater and surface water), and leads to a deterioration in aquatic environments. In the domain of vineyard precision spraying research, one of the most important objectives is to minimize the volume of phytosanitary products. It is also to be more environmentally respectful with more effective vine leaf treatments. Thus the main goal is to be sure that the spray reaches the target to reduce losses that occur. Mechanisms of losses by drift are now well known, in contrary of runoffs on the leaves. To enable a better decision making by the wine-grower in order to optimize the spraying management, it is essential to provide a set of information on basic parameters such as diameter and speed of the droplet. These last ones are used in the calculation of the Weber number. The Weber number is a dimensionless number used in fluid mechanics that is often useful in analysing fluid flows where there is an interface between two different fluids, especially for multiphase flows with strongly curved surfaces. It can be thought of as a measure of the relative importance of fluid's inertia compared to its surface tension. To go further in the analysis of the droplet's behaviour after the impact with the leaf, the contribution of motion information obtained thanks to high speed imaging technology is a relevant solution. In the past, the different behaviours such as adhesion, bounce or splash were manually determined by the observer. Our tracking method based on "active contours" technic allows us to automatically detect the behaviour and to collect informations about the droplet in order to compute its Weber number

Texture classificatication in a cloud computing environment for leaf roughness characterization in precision spraying applications

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Noise Robustness of a Texture Classification Protocol for Natural Leaf Roughness Characterisation

International audienceIn the context of leaf roughness study for precision spray- ing applications, this article deals with its characterisation by computer vision techniques. Texture analysis is a pri- mordial step for applications based on image analysis such as medical or agronomical imaging. The aim is to classify textures after extraction of discriminating features. How- ever, this problem remains complex in the case of natural leaves because of changes in lighting, scaling or orienta- tion. There we consider a family of invariants from the frequency domain called Generalized Fourier Descriptors whose dimensionality is proportional to the spatial resolu- tion of the images. These features used with a Support Vec- tor Machines classifier lead to good results in terms of clas- sification error rate when the dimensionality is small but it gives more errors when the dimensionality increases; we use there different kinds of dimensionality reduction tech- niques (linear or non-linear) whose aim is to keep most in- formation in a vector of small dimensionality. It implies losses of information even if small. This is not the only source of losses, another one is related to the noise present in the images due to acquisition conditions and sensor sen- sitivity. We propose here to demonstrate the robustness of our method of classification despite these losses of infor- mation

Myocardial Infarction Segmentation From Late Gadolinium Enhancement MRI By Neural Networks and Prior Information

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