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34 results on '"Cieslak, Mikolaj"'

1. Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola

Author

Khan, Nazifa Azam, Cieslak, Mikolaj, and McQuillan, Ian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Artificial neural networks are often used to identify features of crop plants. However, training their models requires many annotated images, which can be expensive and time-consuming to acquire. Procedural models of plants, such as those developed with Lindenmayer-systems (L-systems) can be created to produce visually realistic simulations, and hence images of plant simulations, where annotations are implicitly known. These synthetic images can either augment or completely replace real images in training neural networks for phenotyping tasks. In this paper, we systematically vary amounts of real and synthetic images used for training in both maize and canola to better understand situations where synthetic images generated from L-systems can help prediction on real images. This work also explores the degree to which realism in the synthetic images improves prediction. We have five different variants of a procedural canola model (these variants were created by tuning the realism while using calibration), and the deep learning results showed how drastically these results improve as the canola synthetic images are made to be more realistic. Furthermore, we see how neural network predictions can be used to help calibrate L-systems themselves, creating a feedback loop.

Published
2024

2. LAESI: Leaf Area Estimation with Synthetic Imagery

Author

Kałużny, Jacek, Schreckenberg, Yannik, Cyganik, Karol, Annighöfer, Peter, Pirk, Sören, Michels, Dominik L., Cieslak, Mikolaj, Assaad-Gerbert, Farhah, Benes, Bedrich, and Pałubicki, Wojciech

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Machine Learning, 68T07, 68T45, I.2.10, I.4.6
Abstract

We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resource for leaf morphology analysis primarily aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area prediction and semantic segmentation, using real images for validation. Our validation shows that these models can be trained to predict leaf surface area with a relative error not greater than an average human annotator. LAESI also provides an efficient framework based on 3D procedural models and generative AI for the large-scale, controllable generation of data with potential further applications in agriculture and biology. We evaluate the inclusion of generative AI in our procedural data generation pipeline and show how data filtering based on annotation consistency results in datasets which allow training the highest performing vision models., Comment: 10 pages, 12 figures, 1 table

Published
2024

3. Generating Diverse Agricultural Data for Vision-Based Farming Applications

Author

Cieslak, Mikolaj, Govindarajan, Umabharathi, Garcia, Alejandro, Chandrashekar, Anuradha, Hädrich, Torsten, Mendoza-Drosik, Aleksander, Michels, Dominik L., Pirk, Sören, Fu, Chia-Chun, and Pałubicki, Wojciech

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Machine Learning, 68T07, 68T45, I.2.10, I.4.6
Abstract

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture, such as semantic segmentation for autonomous weed control. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models., Comment: 10 pages, 8 figures, 3 tables

Published
2024

5. The hidden diversity of vascular patterns in flower heads.

Author

Owens, Andrew, Zhang, Teng, Gu, Philmo, Hart, Jeremy, Stobbs, Jarvis, Cieslak, Mikolaj, Elomaa, Paula, and Prusinkiewicz, Przemyslaw

Subjects
CARDIOVASCULAR system, HAPTIC devices, VIRTUAL reality software, COMMON sunflower, SPATIAL arrangement
Abstract

Summary: Vascular systems are intimately related to the shape and spatial arrangement of the plant organs they support. We investigate the largely unexplored association between spiral phyllotaxis and the vascular system in Asteraceae flower heads.We imaged heads of eight species using synchrotron‐based X‐ray micro‐computed tomography and applied original virtual reality and haptic software to explore head vasculature in three dimensions. We then constructed a computational model to infer a plausible patterning mechanism.The vascular system in the head of the model plant Gerbera hybrida is qualitatively different from those of Bellis perennis and Helianthus annuus, characterized previously. Cirsium vulgare, Craspedia globosa, Echinacea purpurea, Echinops bannaticus, and Tanacetum vulgare represent variants of the Bellis and Helianthus systems. In each species, the layout of the main strands is stereotypical, but details vary. The observed vascular patterns can be generated by a common computational model with different parameter values.In spite of the observed differences of vascular systems in heads, they may be produced by a conserved mechanism. The diversity and irregularities of vasculature stand in contrast with the relative uniformity and regularity of phyllotactic patterns, confirming that phyllotaxis in heads is not driven by the vasculature. See also the Commentary on this article by Burian & Kierzkowski, 243: 10–13. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Phyllotactic patterning of gerbera flower heads.

Author

Teng Zhang, Cieslak, Mikolaj, Owens, Andrew, Feng Wang, Broholm, Suvi K., Teeri, Teemu H., Elomaa, Paula, and Prusinkiewicz, Przemyslaw

Subjects
*GERBERA, *ASTERACEAE, *PHYLLOTAXIS, *FLOWERS, *FLOWER development
Abstract

Phyllotaxis, the distribution of organs such as leaves and flowers on their support, is a key attribute of plant architecture. The geometric regularity of phyllotaxis has attracted multidisciplinary interest for centuries, resulting in an understanding of the patterns in the model plants Arabidopsis and tomato down to the molecular level. Nevertheless, the iconic example of phyllotaxis, the arrangement of individual florets into spirals in the heads of the daisy family of plants (Asteraceae), has not been fully explained. We integrate experimental data and computational models to explain phyllotaxis in Gerbera hybrida. We show that phyllotactic patterning in gerbera is governed by changes in the size of the morphogenetically active zone coordinated with the growth of the head. The dynamics of these changes divides the patterning process into three phases: the development of an approximately circular pattern with a Fibonacci number of primordia near the head rim, its gradual transition to a zigzag pattern, and the development of a spiral pattern that fills the head on the template of this zigzag pattern. Fibonacci spiral numbers arise due to the intercalary insertion and lateral displacement of incipient primordia in the first phase. Our results demonstrate the essential role of the growth and active zone dynamics in the patterning of flower heads. [ABSTRACT FROM AUTHOR]

Published
2021
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26. Integrating physiology and architecture in models of fruit expansion

Author

Cieslak, Mikolaj, Cheddadi, Ibrahim, Boudon, Frédéric, Baldazzi, Valentina, Génard, Michel, Godin, Christophe, Bertin, Nadia, Cieslak, Mikolaj, Cheddadi, Ibrahim, Boudon, Frédéric, Baldazzi, Valentina, Génard, Michel, Godin, Christophe, and Bertin, Nadia

Abstract

Architectural properties of a fruit, such as its shape, vascular patterns, and skin morphology, play a significant role in determining the distributions of water, carbohydrates, and nutrients inside the fruit. Understanding the impact of these properties on fruit quality is difficult because they develop over time and are highly dependent on both genetic and environmental controls. We present a 3D functional-structural fruit model that can be used to investigate effects of the principle architectural properties on fruit quality. We use a three step modeling pipeline in the OpenAlea platform: (1) creating a 3D volumetric mesh representation of the internal and external fruit structure, (2) generating a complex network of vasculature that is embedded within this mesh, and (3) integrating aspects of the fruit's function, such as water and dry matter transport, with the fruit's structure. We restrict our approach to the phase where fruit growth is mostly due to cell expansion and the fruit has already differentiated into different tissue types. We show how fruit shape affects vascular patterns and, as a consequence, the distribution of sugar/water in tomato fruit. Furthermore, we show that strong interaction between tomato fruit shape and vessel density induces, independently of size, an important and contrasted gradient of water supply from the pedicel to the blossom end of the fruit. We also demonstrate how skin morphology related to microcracking distribution affects the distribution of water and sugars inside nectarine fruit. Our results show that such a generic model permits detailed studies of various, unexplored architectural features affecting fruit quality development.

Published
2016

28. Modeling dense inflorescences

Author

Owens, Andrew, primary, Cieslak, Mikolaj, additional, Hart, Jeremy, additional, Classen-Bockhoff, Regine, additional, and Prusinkiewicz, Przemyslaw, additional

Published
2016
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29. Integrating architecture and physiological perspectives in fruit development

Author

Cieslak, Mikolaj, Génard, Michel, Boudon, Frédéric, Baldazzi, Valentina, Godin, Christophe, Bertin, Nadia, Department of Computer Science [Calgary] (CPSC), University of Calgary, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de la Recherche Agronomique (INRA), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Agropolis Fondation, Risto Sievänen and Eero Nikinmaa and Christophe Godin and Anna Lintunen and Pekka Nygren, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, skin microcracking, caractérisation physiologique, nectarine, F62 - Physiologie végétale - Croissance et développement, fruit vasculature, F50 - Anatomie et morphologie des plantes, qualité du fruit, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, composition biochimique du fruit, ComputingMilieux_MISCELLANEOUS, modélisation, Prunus persica, Fruit quality, Vegetal Biology, U10 - Informatique, mathématiques et statistiques, modèle 3d, tissu vasculaire, food and beverages, water and carbon transport, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, modèle informatique, Agricultural sciences, métabolisme du carbone, transport d'eau, Biologie végétale, Sciences agricoles, fruit quality, architecture de la plante
Abstract

ISBN 978-951-651-408-9265170; International audience; Architectural properties of a fruit, such as its shape, vascular patterns, and skin morphology, play a significant role in determining the distributions of water, carbohydrates, and nutrients inside the fruit. Understanding the impact of these properties on fruit quality is difficult, because they develop over time and are highly dependent on both genetic and environmental controls. We developed a 3D fruit model that can be used to investigate effects of the principle architectural properties on fruit quality.

Published
2013

34. Computational Models of Auxin-Driven Patterning in Shoots.

Author

Cieslak M, Owens A, and Prusinkiewicz P

Subjects
Biological Transport, Computer Simulation, Membrane Transport Proteins metabolism, Plants metabolism, Indoleacetic Acids metabolism, Models, Biological, Plant Development
Abstract

Auxin regulates many aspects of plant development and behavior, including the initiation of new outgrowth, patterning of vascular systems, control of branching, and responses to the environment. Computational models have complemented experimental studies of these processes. We review these models from two perspectives. First, we consider cellular and tissue-level models of interaction between auxin and its transporters in shoots. These models form a coherent body of results exploring different hypotheses pertinent to the patterning of new outgrowth and vascular strands. Second, we consider models operating at the level of plant organs and entire plants. We highlight techniques used to reduce the complexity of these models, which provide a path to capturing the essence of studied phenomena while running simulations efficiently., (Copyright © 2022 Cold Spring Harbor Laboratory Press; all rights reserved.)

Published
2022
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