Relevant bibliographies by topics / Plant segmentation

Academic literature on the topic 'Plant segmentation'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Plant segmentation"

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Murray, Carl, and Mark O'Malley. "Segmentation of plant cell pictures." Image and Vision Computing 11, no. 3 (April 1993): 155–62. http://dx.doi.org/10.1016/0262-8856(93)90054-k.

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Mahajan, Vatsal, Dilip Jain, and Abhinav Dua. "Plant Leaf Segmentation Invariant of Background." International Journal of Computer & Organization Trends 12, no. 1 (September 25, 2014): 24–26. http://dx.doi.org/10.14445/22492593/ijcot-v12p305.

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Diao, Zhi Hua, Yin Mao Song, Huan Wang, and Yun Peng Wang. "Study Surveys on Image Segmentation of Plant Disease Spot." Advanced Materials Research 542-543 (June 2012): 1047–50. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1047.

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Segmentation is a fundamental component of many image-processing applications. Various algorithms were proposed so far for segmentation of plant disease images. The researchers raised some corresponding solutions to different characteristics of disease spot, and these algorithms are continually improved to enhance the speed and veracity. Based on current progress, this paper gives a study on the image segmentation classification. In addition, this article also makes a comprehensive expatiation on how to solve the problem of plant disease spot by using image segmentation techniques. In the end, open problems and future trend of segmentation algorithm were discussed.
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钟, 旭升. "Segmentation of Plant Point Cloud Segmentation Based on Dynamic Graph Convolution Network." Computer Science and Application 12, no. 03 (2022): 690–96. http://dx.doi.org/10.12677/csa.2022.123070.

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Cao, Liying, Hongda Li, Helong Yu, Guifen Chen, and Heshu Wang. "Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network." Applied Engineering in Agriculture 37, no. 5 (2021): 929–40. http://dx.doi.org/10.13031/aea.14495.

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HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.
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Et. al., Rajendra Prasad Bellapu,. "PERFORMANCE COMPARISON OF UNSUPERVISED SEGMENTATION ALGORITHMS ON RICE, GROUNDNUT, AND APPLE PLANT LEAF IMAGES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 13, 2021): 1090–105. http://dx.doi.org/10.17762/itii.v9i2.457.

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This paper focuses on plant leaf image segmentation by considering the aspects of various unsupervised segmentation techniques for automatic plant leaf disease detection. The segmented plant leaves are crucial in the process of automatic disease detection, quantification, and classification of plant diseases. Accurate and efficient assessment of plant diseases is required to avoid economic, social, and ecological losses. This may not be easy to achieve in practice due to multiple factors. It is challenging to segment out the affected area from the images of complex background. Thus, a robust semantic segmentation for automatic recognition and analysis of plant leaf disease detection is extremely demanded in the area of precision agriculture. This breakthrough is expected to lead towards the demand for an accurate and reliable technique for plant leaf segmentation. We propose a hybrid variant that incorporates Graph Cut (GC) and Multi-Level Otsu (MOTSU) in this paper. We compare the segmentation performance implemented on rice, groundnut, and apple plant leaf images for various unsupervised segmentation algorithms. Boundary Displacement error (BDe), Global Consistency error (GCe), Variation of Information (VoI), and Probability Rand index (PRi), are the index metrics used to evaluate the performance of the proposed model. By comparing the outcomes of the simulation, it demonstrates that our proposed technique, Graph Cut based Multi-level Otsu (GCMO), provides better segmentation results as compared to other existing unsupervised algorithms.
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Sun, Guiling, Xinglong Jia, and Tianyu Geng. "Plant Diseases Recognition Based on Image Processing Technology." Journal of Electrical and Computer Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/6070129.

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A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.
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Wang, Yi, and Lihong Xu. "Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation." PeerJ 6 (June 28, 2018): e5036. http://dx.doi.org/10.7717/peerj.5036.

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Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.
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Guadarrama, Lili, Carlos Paredes, and Omar Mercado. "Plant Disease Diagnosis in the Visible Spectrum." Applied Sciences 12, no. 4 (February 20, 2022): 2199. http://dx.doi.org/10.3390/app12042199.

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A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios.
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Li, Dawei, Jinsheng Li, Shiyu Xiang, and Anqi Pan. "PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants." Plant Phenomics 2022 (May 23, 2022): 1–20. http://dx.doi.org/10.34133/2022/9787643.

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Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.
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Dissertations / Theses on the topic "Plant segmentation"

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Wang, Shisheng. "Plant segmentation for growth analysis in temporal datasets." Thesis, Aberystwyth University, 2018. http://hdl.handle.net/2160/1d6683d9-a530-416a-b0e8-594b87ecb684.

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High-throughput phenotyping is an important means to meet the agricultural needs for future food and energy production. This entails an increasing amount of work in Image-based, non-destructive phenotyping systems. This thesis de-scribes a low-cost phenotype collection system for growth chambers, and methods to segment plants from time series images using temporal information. The system uses a webcam to record plant growth in a top-down view with a fixed time interval to create time-lapse images of multiple plants. It has successfully recorded the growth of Arabidopsis thaliana over three months from seedling to flower. The development of plant segmentation methods involves experiments to compare and select the optimal colour space for plant segmentation, and the development of an unsupervised plant segmentation method that is capable of segmenting multiple plant species (e.g. Arabidopsis thaliana, Oats, Oilseed Rape) without relying on knowledge of plant colour. The method is also modified to provide colour-based, superpixel-based and supervoxel-based approaches to the segmentation of plants from time series images.
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Dammannagari, Gangadhara Shravan. "Mobile high-throughput phenotyping using watershed segmentation algorithm." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35387.

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Master of Science
Department of Computing and Information Sciences
Mitchell L. Neilsen
This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).
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Cerutti, Guillaume. "Segmentation et interprétation d'images naturelles pour l'identification de feuilles d'arbres sur smartphone." Thesis, Lyon 2, 2013. http://www.theses.fr/2013LYO22022/document.

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Les espèces végétales, et en particulier les espèces d'arbres, forment un cadre de choix pour un processus de reconnaissance automatique basé sur l'analyse d'images. Les critères permettant de les identifier sont en effet le plus souvent des éléments morphologiques visuels, bien décrits et référencés par la botanique, qui laissent à penser qu'une reconnaissance par la forme est envisageable. Les feuilles constituent dans ce contexte les organes végétaux discriminants les plus faciles à appréhender, et sont de ce fait les plus communément employés pour ce problème qui connaît actuellement un véritable engouement. L'identification automatique pose toutefois un certain nombre de problèmes complexes, que ce soit dans le traitement des images ou dans la difficulté même de la classification en espèces, qui en font une application de pointe en reconnaissance de formes.Cette thèse place le problème de l'identification des espèces d'arbres à partir d'images de leurs feuilles dans le contexte d'une application pour smartphones destinée au grand public. Les images sur lesquelles nous travaillons sont donc potentiellement complexes et leur acquisition peu supervisée. Nous proposons alors des méthodes d'analyse d'images dédiées, permettant la segmentation et l'interprétation des feuilles d'arbres, en se basant sur une modélisation originale de leurs formes, et sur des approches basées modèles déformables. L'introduction de connaissances a priori sur la forme des objets améliore ainsi de façon significative la qualité et la robustesse de l'information extraite de l'image. Le traitement se déroulant sur l'appareil, nous avons développé ces algorithmes en prenant en compte les contraintes matérielles liées à leur utilisation.Nous introduisons également une description spécifique des formes des feuilles, inspirée par les caractéristiques déterminantes recensées dans les ouvrages botaniques. Ces différents descripteurs fournissent des informations de haut niveau qui sont fusionnées en fin de processus pour identifier les espèces, tout en permettant une interprétation sémantique intéressante dans le cadre de l'interaction avec un utilisateur néophyte. Les performances obtenues en termes de classification, sur près de 100 espèces d'arbres, se situent par ailleurs au niveau de l'état de l'art dans le domaine, et démontrent une robustesse particulière sur les images prises en environnement naturel. Enfin, nous avons intégré l'implémentation de notre système de reconnaissance dans l'application Folia pour iPhone, qui constitue une validation de nos approches et méthodes dans un cadre réel
Plant species, and especially tree species, constitute a well adapted target for an automatic recognition process based on image analysis. The criteria that make their identification possible are indeed often morphological visual elements, which are well described and referenced by botany. This leads to think that a recognition through shape is worth considering. Leaves stand out in this context as the most accessible discriminative plant organs, and are subsequently the most often used for this problem recently receiving a particular attention. Automatic identification however gives rise to a fair amount of complex problems, linked with the processing of images, or in the difficult nature of the species classification itself, which make it an advanced application for pattern recognition.This thesis considers the problem of tree species identification from leaf images within the framework of a smartphone application intended for a non-specialist audience. The images on which we expect to work are then potentially very complex scenes and their acquisition rather unsupervised. We consequently propose dedicated methods for image analysis, in order to segment and interpret tree leaves, using an original shape modelling and deformable templates. The introduction on prior knowledge on the shape of objects enhances significatively the quality and the robustness of the information we extract from the image. All processing being carried out on the mobile device, we developed those algorithms with concern towards the material constraints of their exploitation. We also introduce a very specific description of leaf shapes, inspired by the determining characteristics listed in botanical references. These different descriptors constitute independent sources of high-level information that are fused at the end of the process to identify species, while providing the user with a possible semantic interpretation. The classification performance demonstrated over approximately 100 tree species are competitive with state-of-the-art methods of the domain, and show a particular robustness to difficult natural background images. Finally, we integrated the implementation of our recognition system into the \textbf{Folia} application for iPhone, which constitutes a validation of our approaches and methods in a real-world use
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von, Koch Christian, and William Anzén. "Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation models." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177269.

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Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision.  There are many prominent convolutional neural network architectures in the literature, and this thesis explores two: a modified U-Net and the PSPNet. The architectures were combined with three loss functions and three class weighting schemes resulting in 18 model configurations that were evaluated and compared. This thesis also explores transfer learning techniques for neural networks tasked with identifying slag in images from inside a furnace. The benefit of transfer learning is that the network can learn to find features from already labeled data of another context. Finally, the thesis explored how temporal information could be utilised by adding an LSTM layer to a model taking pairs of images as input, instead of one. The results show (1) that the PSPNet outperformed the U-Net for all tested configurations in all relevant metrics, (2) that the model is able to find more complex features while converging quicker by using transfer learning, and (3) that utilising temporal information reduced the variance of the predictions, and that the modified PSPNet using an LSTM layer showed promise in handling images with outlying characteristics.
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Bertrand, Sarah. "Analyse d'images pour l'identification multi-organes d'espèces végétales." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2127/document.

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Cette thèse s’inscrit dans le cadre de l’ANR ReVeRIES dont l’objectif est d’utiliser les technologies mobiles pour aider l’homme à mieux connaître son environnement et notamment les végétaux qui l’entourent. Plus précisément, le projet ReVeRIES s’appuie sur une application mobile, nommée Folia développée dans le cadre du projet ANR ReVeS, capable de reconnaître les espèces d’arbres et arbustes à partir de photos de leurs feuilles. Ce prototype se différencie des autres outils car il est capable de simuler le comportement du botaniste. Dans le contexte du projet ReVeRIES, nous nous proposons d’aller beaucoup plus loin en développant de nouveaux aspects : la reconnaissance multimodale d’espèces, l’apprentissage par le jeu et les sciences citoyennes. L’objet de cette thèse porte sur le premier de ces trois aspects, à savoir l’analyse d’images d’organes de végétaux en vue de l’identification.Plus précisément, nous considérons les principaux arbres et arbustes, endémiques ou exotiques, que l’on trouve en France métropolitaine. L’objectif de cette thèse est d’étendre l’algorithme de reconnaissance en prenant en compte d’autres organes que la feuille. Cette multi-modalité est en effet essentielle si nous souhaitons que l’utilisateur apprenne et s’entraîne aux différentes méthodes de reconnaissance, pour lesquelles les botanistes utilisent la variété des organes (i.e. les feuilles, les fleurs, les fruits et les écorces). La méthode utilisée par Folia pour la reconnaissance des feuilles étant dédiée, car simulant le botaniste, ne peut s’appliquer directement aux autres organes. Ainsi, de nouveaux verrous se posent, tant au niveau dutraitement des images qu’au niveau de la fusion de données.Une première partie de la thèse a été consacrée à la mise en place de méthodes de traitement d’images pour l’identification des espèces végétales. C’est l’identification des espèces d’arbres à partir d’images d’écorces qui a été étudiée en premier. Les descripteurs développés prennent en compte la structure de l’écorce en s’inspirant des critères utilisés par les botanistes. Les fruits et les fleurs ont nécessité une étape de segmentation avant leur description. Une nouvelle méthode de segmentation réalisable sur smartphone a été développée pour fonctionner sur la grande variabilité des fleurs et des fruits. Enfin, des descripteurs ont été extraits sur les fruits et les fleurs après l’étape de segmentation. Nous avons décidé de ne pas faire de séparation entre les fleurs et les fruits car nous avons montré qu’un utilisateur novice en botanique ne sait pas toujours faire la différence entre ces deux organes sur des arbres dits «d’ornement» (non fruitiers). Pour les fruits et les fleurs, la prédiction n’est pas seulement faite sur les espèces mais aussi sur les genres et les familles, groupes botaniques traduisant d’une similarité entre ces organes.Une deuxième partie de la thèse traite de la combinaison des descripteurs des différents organes que sont les feuilles, les écorces, les fruits et les fleurs. En plus des méthodes de combinaison basiques, nous proposons de prendre en compte la confusion entre les espèces, ainsi que les prédictions d’appartenance aux taxons botaniques supérieurs à l’espèce.Enfin, un chapitre d’ouverture est consacré au traitement de ces images par des réseaux de neurones à convolutions. En effet, le Deep-Learning est de plus en plus utilisé en traitement d’images, notamment appliqué aux organes végétaux. Nous proposons dans ce contexte de visualiser les filtres de convolution extrayant de l’information, afin de faire le lien entre lesinformations extraites par ces réseaux et les éléments botaniques
This thesis is part of the ANR ReVeRIES, which aims to use mobile technologies to help people better understand their environment and in particular the plants that surround them. More precisely, the ReVeRIES project is based on a mobile application called Folia developed as part of the ANR ReVeS project and capable of recognising tree and shrub species based on photos of their leaves. This prototype differs from other tools in that it is able to simulate the behaviour of the botanist. In the context of the ReVeRIES project, we propose to go much further by developing new aspects: multimodal species recognition, learning through play and citizen science. The purpose of this thesis is to focus on the first of these three aspects, namelythe analysis of images of plant organs for identification.More precisely, we consider the main trees and shrubs, endemic or exotic, found in metropolitan France. The objective of this thesis is to extend the recognition algorithm by taking into account other organs in addition to the leaf. This multi-modality is indeed essential if we want the user to learn and practice the different methods of recognition for which botanists use the variety of organs (i.e. leaves, flowers, fruits and bark). The method used by Folia for leaf recognition being dedicated, because simulating the work of a botanist on the leaf, cannot be applied directly to other organs. Thus, new challenges are emerging, both in terms of image processing and data fusion.The first part of the thesis was devoted to the implementation of image processing methods for the identification of plant species. The identification of tree species from bark images was the first to be studied. The descriptors developed take into account the structure of the bark inspired from the criteria used by botanists. Fruits and flowers required a segmentation step before their description. A new segmentation method that can be used on smartphones has been developed to work in spite of the high variability of flowers and fruits. Finally, descriptors were extracted on fruits and flowers after the segmentation step. We decided not to separate flowers and fruits because we showed that a user new to botany does not always know the difference between these two organs on so-called "ornamental" trees (not fruit trees). For fruits and flowers, prediction is not only made on their species but also on their genus and family, botanical groups reflecting a similarity between these organs.The second part of the thesis deals with the combination of descriptors of the different organs: leaves, bark, fruits and flowers. In addition to basic combination methods, we propose to consider the confusion between species, as well as predictions of affiliations in botanical taxa higher than the species.Finally, an opening chapter is devoted to the processing of these images by convolutional neural networks. Indeed, Deep Learning is increasingly used in image processing, particularly for plant organs. In this context, we propose to visualize the learned convolution filters extracting information, in order to make the link between the information extracted by these networks and botanical elements
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Pollak, Williamson Bernardo. "Frameworks for reprogramming early diverging land plants." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273535.

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Plant form is a product of emergent processes of cell division, patterning and morphogenesis. These fundamental processes remain poorly characterised in plants. However, engineering approaches can provide new tools and frameworks for the study and manipulation of plant development. This dissertation describes the development of engineering frameworks for reprogramming of the early diverging land plant Marchantia polymorpha (Marchantia). I describe the generation of genomic and transcriptomic datasets for Marchantia, which has provided the basis for the compilation of a gene-centric registry of DNA parts for engineering (MarpoDB). I describe the development of Loop assembly, an efficient and standardised DNA assembly system based on Type IIS restriction enzymes for recursive fabrication of DNA circuits with high efficiency. MarpoDB was used to mine new DNA parts compatible with Loop assembly which were used to generate plant transformation vectors for labelling of cellular features to study aspects of growth and development. I performed image analysis of genetic markers for segmentation and quantification of cellular properties in germinating gemmae. I implemented high-efficiency Cas9-mediated mutagenesis in Marchantia for use in functional molecular genetics studies. Furthermore, I produced inducible systems for expression of heterologous elements by transactivation which showed negligible levels of basal activity. It was possible to use this system for induction of gene expression in single cells. Finally, these new frameworks were applied to study the gametophytic meristem in Marchantia gemmae. I mapped the expression of several putative candidate homologues for higher plant meristem regulators, performed overexpression and loss-of-function studies for homologues of WUSCHEL, CLAVATA3 and SHOOT MERISTEMLESS. A strategy for misregulation of endogenous genes was developed using inducible transactivation, and was used with cellular markers for WUSCHEL and CLAVATA3 homologues in Marchantia.
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Kenouche, Samir. "Études expérimentales et modélisation de la dynamique de distribution des agents de contraste en imagerie RMN : applications à l'agronomie." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2013. http://tel.archives-ouvertes.fr/tel-01019641.

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Les études non destructives des processus physiologiques dans les produits agronomiques exigent des résolutions spatiales et temporelles de plus en plus élevées. L'imagerie par résonance magnétique nucléaire (RMN) est une technique totalement non-invasive qui permet d'accéder à plusieurs types de variables (architecture des tissus, variabilités spatiales de la composition, flux entrants et internes au cours de la croissance du fruit) plus difficilement quantifiables avec des méthodes destructives classiques. Un des enjeux majeur également réside dans la faculté de localiser spatialement ces transformations physiologiques et morphologiques dans les produits agronomiques. Les travaux de recherches réalisés dans le cadre de cette thèse ont pour objectif principal, la mise en œuvre d'une méthodologie de calcul et d'analyse quantitative en imagerie RMN appliquée à l'agronomie. L'implémentation, l'optimisation et la validation de la séquence FLASH combinée avec des agents de contraste efficaces en terme de relaxivité et bio-compatibles a permis d'une part, la cartographie des paramètres de relaxation et d'autre part, la quantification du transport de l'eau in vivo d'un système agronomique modèle au cours de sa croissance. Les nanoparticules de l'agent de contraste Gd3+[Fe(CN)6]3-/Mannitol ont été utilisées comme des marqueurs afin de localiser les flux hydriques dans le fruit. Le choix de la séquence d'imagerie FLASH a été motivé par la nécessite d'atteindre des résolutions temporelles suffisante pour suivre la dynamique des changements physiologiques liés au transport de l'eau dans ce type de matériau. La validation de la méthode de calcul du T1 menée sur le fantôme a révélé un bon accord par rapport aux T1 mesurés par relaxométrie. Nous avons également mis au point une procédure d'évaluation du rapport signal sur bruit et des incertitudes commises dans chaque voxel des images paramétriques M0 et T1. L'évaluation de ces incertitudes est un élément fondamental de cette analyse quantitative, afin d'assurer des interprétations fiables des images RMN. La segmentation des images nous a permis de localiser précisément les tissus où règne une forte activité cellulaire. Enfin, la modélisation compartimentale mis en oeuvre nous a permis de quantifier les paramètres cinétiques liés au transport de l'eau dans le fruit.Mots-clés: Imagerie RMN quantitative, paramètres intrinsèques, segmentation, modélisation compartimentale, agents de contraste, tissus végétaux
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Ramalingam, Nagarajan. "Non-contact multispectral and thermal sensing techniques for detecting leaf surface wetness." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1104392582.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xxii, 271 p.; also includes graphics (some col.) Includes bibliographical references (p. 206-214).
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Perissini, Ivan Carlos. "Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-25052018-095947/.

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O uso da visão computacional vem ganhando espaço no contexto agrícola, especialmente com a evolução do conceito da agricultura de precisão. Aplicações como irrigação, fertilização e controle de pragas são apenas alguns dos cenários que essa tecnologia pode atender. Entretanto, a demanda por sistemas acessíveis e eficientes aliada às inconstâncias e ruídos visuais de um ambiente externo, apresentam desafios a estes processos. Foi proposto neste trabalho uma análise da literatura e uma série de investidas experimentais de técnicas de processamento de imagens, para buscar melhores relações entre custo computacional e desempenho da detecção de mudas de plantas, visando atingir operações em tempo real com o uso de hardwares comuns e de baixo custo. Para tanto o trabalho investiga a composição de estratégias de segmentação a partir de diferentes espaços de cor e métodos de constância de cor, de forma a reduzir a variação luminosa, uma das maiores fontes de instabilidade nas aplicações de visão na agricultura. Os experimentos propostos foram divididos em duas fases; na primeira o sistema de medidas foi avaliado, definindo as métricas e condições experimentais adequadas para a segunda fase, composta de uma sequência de experimentos comparativos entre estratégias de segmentação sob diferentes condições de iluminação. Os resultados mostraram que as soluções são muito dependentes das condições da cena e uma série de alternativas promissoras de segmentação foram obtidas. Sua elegibilidade, porém, depende de considerações sobre a disponibilidade computacional e contexto de aplicação.
The use of computer vision has been gaining ground in the agricultural context, especially with the evolution of the concept of precision agriculture. Applications such as irrigation, fertilization and pest control are just some of the scenarios that this technology can be used. However, the demand for accessible and efficient systems together with the variations and visual noise from an external environment presents challenges to these processes. It was proposed in this study an analysis of the literature and a series of experimental investigations of image processing techniques, to search for better relations between computational cost and performance in the detection of seedlings, aiming to achieve real time operations with the use of common and low cost hardware. For this, the work investigates the composition of segmentation strategies from different color spaces and color constancy methods, in order to combat light variation, one of the major sources of instability in agricultural vision applications. The proposed experiments were divided into two phases; in the first the measurement system was evaluated, defining the metrics and suitable conditions for the experiments at second phase, composed of a sequence of comparative experiments of segmentation strategies under different lighting conditions. The results showed that the solutions are very dependent on the conditions of the scene and a series of promising segmentation alternatives were obtained. Their eligibility, however, depends on considerations about the computational availability and context of the application.
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Lin, Meng-Yen. "Assessing market segmentation success : developing a plan, fieldwork, action approach." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/36179/.

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Market segmentation practice has been one of the central issues in marketing research over the past thirty years. However, the results of many segmentation studies have been unworkable from a business stand-point. This research was concerned with understanding what makes some market segmentation projects more successful than others. The purpose was to examine the relationship between possible success factors and the success of a segmentation project. The processes of the research included: identifying a range of factors which may impact on the success of market segmentation; hypothesising and testing relationships between these factors and market segmentation success; developing the plan, fieldwork, action (PFA) model for assessing market segmentation success; and generating recommendations for relevant modifications that will improve the odds of market segmentation success. The research proceeded in a series of three interrelated phases: qualitative first, quantitative next, and then qualitative again. In the first phase, an initial list of the critical factors for segmentation success was generated through a review of the literature. The list was then validated and expanded by pilot interviews with marketing managers. In the second phase, a questionnaire was developed for gathering the necessary empirical data. 600 questionnaires were handed out at the Birmingham National Exhibition Centre at eight different trade shows. 221 usable responses were returned. Using the SPSS package, univariate, bivariate as well as multivariate statistics were employed to analyse the data. Lastly, validating interviews were conducted in an attempt to explain the research findings. Ten factors believed to impact upon segmentation success were extracted. Seven of them were found to be critical to segmentation success and were termed critical success factors (CSFs). In addition, the research also identified the plan, fieldwork and action (PFA) stages in the segmentation process which led to the development of the PFA model. The model can be used to explain why some segmentation projects are successful while others are not. It was found that the plan and action stages were those most likely to impact upon segmentation success. The managerial implications of the research findings were discussed and suggestions for further research were proposed.
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Books on the topic "Plant segmentation"

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Bank Marketing Association (U.S.), ed. Building a financial services marketing plan: Working plans for product and segment marketing. Naperville, Ill: Financial Sourcebooks, 1989.

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Medicinal and Aromatic Plants Strategic Segmentation Analysis. World Bank, Washington, DC, 2018. http://dx.doi.org/10.1596/31613.

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Castellani, Claudia, and Marianne Wootton. Crustacea: Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0021.

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This chapter provides an introduction to the Crustacea, one of the most abundant and diverse components of the plankton. Within a single net-haul, the vast diversity within this group, coupled with the large number of species and the morphological similarity both between species and between developmental stages, can often pose a significant identification challenge even to experienced taxonomists. Although all Crustacea originally share a common body plan, their morphology can differ quite markedly due to different degrees of expression of body segmentation patterns and as a result of the loss or morphological modifications of paired appendages. There is also considerable variation between groups in the structure and function of the appendages on different body regions.
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Canny, Nicholas, and Philip Morgan. Introduction. Edited by Nicholas Canny and Philip Morgan. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199210879.013.0001.

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Beginning in the fifteenth century, people, plants, pathogens, products, and cultural practices — just to mention some key agents — began to move regularly back and forth across the Atlantic Ocean. As the connections and exchanges deepened and intensified, much was transformed. New peoples, economies, societies, polities, and cultures arose, particularly in the lands and islands touched by that ocean, while others were destroyed. This book describes, explains, and, occasionally, challenges conventional wisdom concerning these path-breaking developments from the late fifteenth to the early nineteenth century. It looks at European conquests of Native American populations (in North and South America), how some Native Americans contributed to the Atlantic trading world that flourished from the later seventeenth century onwards, the slave trade and importation of slaves from Africa, human settlement in America, and the re-segmentation of the Atlantic world of the eighteenth century into multiple polities.
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Book chapters on the topic "Plant segmentation"

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Aparna, S., and R. Aarthi. "Segmentation of Tomato Plant Leaf." In Advances in Intelligent Systems and Computing, 149–56. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3373-5_14.

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F. Danilevicz, Monica, and Philipp Emanuel Bayer. "Machine Learning for Image Analysis: Leaf Disease Segmentation." In Plant Bioinformatics, 429–49. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_22.

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Guedes-Pinto, H., O. Pinto-Carnide, and F. Leal. "Segmentation Effect of Immature Spike on Triticale Calli Induction." In Plant Aging, 361–65. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-5760-5_46.

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Guo, Wei, and Akshay L. Chandra. "Deep Learning in Plant Omics: Object Detection and Image Segmentation." In Plant Omics, 234–45. GB: CABI, 2022. http://dx.doi.org/10.1079/9781789247534.0018.

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Fernàndez, G., M. Kunt, and J. P. Zrÿd. "A new plant cell image segmentation algorithm." In Image Analysis and Processing, 229–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_263.

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Choudhury, Sruti Das. "Segmentation Techniques and Challenges in Plant Phenotyping." In Intelligent Image Analysis for Plant Phenotyping, 69–92. First edition. | Boca Raton, FL : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315177304-6.

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Peng, Chen, Chuanliang Cheng, and Ling Wang. "Intelligent Segmentation of Furnace Flame Image." In Reconstruction and Intelligent Control for Power Plant, 39–63. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5574-7_3.

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Heiwolt, Karoline, Tom Duckett, and Grzegorz Cielniak. "Deep Semantic Segmentation of 3D Plant Point Clouds." In Towards Autonomous Robotic Systems, 36–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89177-0_4.

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Körschens, Matthias, Paul Bodesheim, Christine Römermann, Solveig Franziska Bucher, Mirco Migliavacca, Josephine Ulrich, and Joachim Denzler. "Weakly Supervised Segmentation Pretraining for Plant Cover Prediction." In Lecture Notes in Computer Science, 589–603. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5_38.

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Agarwal, Mohit, Suneet Kr Gupta, and K. K. Biswas. "Plant Leaf Disease Segmentation Using Compressed UNet Architecture." In Lecture Notes in Computer Science, 9–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75015-2_2.

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Conference papers on the topic "Plant segmentation"

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Paturkar, Abhipray, Gourab Sen Gupta, and Donald Bailey. "Plant Trait Segmentation for Plant Growth Monitoring." In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2020. http://dx.doi.org/10.1109/ivcnz51579.2020.9290575.

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Dias, Jeferson de Souza, and José Hiroki Saito. "Coffee plant image segmentation and disease detection using JSEG algorithm." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18887.

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Brazil is the largest coffee producer in the world, and then there are many challenges to maintain the high quality and purity of the beans. Thus, it is important to study coffee plants, and help agronomists to detect diseases, such as rust, with resources of computer science. In this work, it is described experiments using image segmentation algorithm JSEG, which is capable to segment images in multi-scale. Using a coffee tree image database RoCoLe (Robusta Coffee Leaf Images), the JSEG algorithm is used to segment these images in four scales. It is selected typical segments in each scale and they are grouped using similarity of normalized color histograms. In this way the several scales segmentations are compared. It is concluded that the segments in scales 1 and 2, in which the colors are more homogeneous then in scales 3 and 4, are adequate to use as training samples for the detection of rust diseases.
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Zhong, Changyuan, Zelin Hu, Xuanjiang Yang, Hualong Li, Fei Liu, and Miao Li. "Triple Stream Segmentation Network for Plant Disease Segmentation." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390933.

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Abbasi, Arash, and Noah Fahlgren. "Naïve Bayes pixel-level plant segmentation." In 2016 IEEE Western New York Image and Signal Processing Workshop (WNYISPW). IEEE, 2016. http://dx.doi.org/10.1109/wnyipw.2016.7904790.

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Jung, Joo-Yeon, Sang-Ho Lee, and Jong-Ok Kim. "Plant Leaf Segmentation Using Knowledge Distillation." In 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2022. http://dx.doi.org/10.1109/icce-asia57006.2022.9954844.

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Chau, Zhong Hoo, Ishara Paranawithana, Liangjing Yang, and U.-Xuan Tan. "Plant Cell Segmentation with Adaptive Thresholding." In 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2018. http://dx.doi.org/10.1109/m2vip.2018.8600840.

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"Automated segmentation of plant leaf images." In AGRICULTURAL INFORMATION TECHNOLOGY AND ENGINEERING AGROINFO-2021. SFSCA RAS, 2021. http://dx.doi.org/10.26898/agroinfo-2021-26-30.

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"Applying neural network for the segmentation of spike structural elements." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-049.

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Dayanand, R. B., and Daneshwari A. Noola. "Plant leaf segmentation through connected pixel approach." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987781.

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Rahman, Md Arifur, Md Mukitul Islam, G. M. Shahir Mahdee, and Md Wasi Ul Kabir. "Improved Segmentation Approach for Plant Disease Detection." In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019. http://dx.doi.org/10.1109/icasert.2019.8934895.

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