SKU: 53766906270
extra wide plant pots

extra wide plant pots Extra Large White Concrete Planter for Trees – 100cm Tall the Aurella

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Description

extra wide plant pots Extra Large White Concrete Planter for Trees – 100cm Tall the AurellaXXL Cylinder Planter Ideal for Trees, Architectural Shrubs & Dramatic Feature Plants Instantly elevates grand entrances, landscaped gardens and commercial courtyards. A sculptural statement planter that blends premium materials with timeless design, crafted from high quality pigmented concrete for enduring beauty and functionality. 621L capacity perfect for mature trees, specimen shrubs and large scale displays, providing ample space for extensive

XXL Cylinder Planter – Ideal for Trees, Architectural Shrubs & Dramatic Feature Plants

Instantly elevates grand entrances, landscaped gardens and commercial courtyards.

A sculptural statement planter that blends premium materials with timeless design, crafted from high-quality pigmented concrete for enduring beauty and functionality.


  • 621L capacity – perfect for mature trees, specimen shrubs and large-scale displays, providing ample space for extensive root systems
  • 100cm Ø × 100cm H – with a generous 91cm planting opening for effortless access, facilitating easy planting and maintenance
  • Crafted from premium pigmented concrete – no paint, no peeling, no fading; long-lasting colour integrity and visual appeal
  • Indoor & outdoor use – internally sealed, frost- and UV-resistant, making it suitable for all-season environments
  • Pre-drilled drainage holes – ensure healthy roots and easy water management, promoting strong plant growth
  • Heavy-duty & secure – 134kg empty weight with a steel-reinforced core for maximum stability and theft deterrence
  • No assembly required – arrives ready to place, saving time and effort
  • Low-maintenance – wipe clean; no treatments or sealing needed
  • Flat base – sits securely on any solid, level surface for dependable stability

Tip: Position the Aurella in its final location before adding soil and plants to avoid heavy lifting later.


Why Choose the Aurella Cylinder Planter?

  • Premium pigmented concrete – colour runs through the body, ensuring long-lasting aesthetics and resistance to surface damage
  • Generous 621L soil capacity – supports deep, healthy root systems for large trees, promoting robust plant growth
  • Raw, hand-finished texture – each piece has unique pitting and natural character, adding visual interest and authenticity
  • Engineered strength – steel-reinforced walls resist cracks, knocks and theft, ensuring durability in all conditions
  • Weather-proof design – frost-safe, UV-stable and internally sealed for year-round use
  • Colour integrityWhite Mist is a soft white with grey undertones that won’t flake, peel or fade
  • Sustainable craftsmanship – handmade by skilled artisans with minimal waste, supporting eco-conscious production
  • Works beautifully in multiples – pair with smaller Aurella sizes for layered impact and cohesive garden styling

Full Description

Introducing the Aurella – a bold, cylindrical planter engineered to transform expansive spaces. This impressive concrete vessel, with its tall, perfectly round silhouette and commanding presence, is designed to make a statement in any setting. The 621-litre capacity is ideal for substantial plantings, from mature olive trees and specimen conifers to lush architectural shrubs that demand space to thrive.

Each planter is hand-poured and finished using a premium pigmented concrete blend, resulting in a raw, tactile texture with gentle pitting and natural variation – no two pieces are the same. The White Mist finish – a fusion of soft white and light grey tones – is pigmented throughout the body, meaning the colour will never flake or fade.

Built for real-world durability, the Aurella is internally sealed, frost-resistant, and UV-stable. Discreet drainage holes help maintain healthy root environments, while the steel-reinforced core and 134kg empty weight ensure it remains stable and secure in high-traffic or exposed locations.

Ready to use right out of the box, the Aurella offers architectural elegance, exceptional strength, and low-maintenance performance – ideal for upscale landscaping and design-conscious homeowners.

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SKU: 53766906270

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Louisville, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Par
Bozeman, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Draper, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Natrona Heights, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Battle Creek, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026

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