SKU: 10628511125
blue pearl succulent plant

blue pearl succulent plant SunSparkler® Sedum 'Blue Pearl'

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Ships within 48 hours · Estimated delivery Aug 19 - Aug 24

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Description

blue pearl succulent plant SunSparkler® Sedum 'Blue Pearl'Blue Pearl is a Sedum hybrid developed by Chris Hansen, a plant breeder fascinated with these ultra hardy perennials. This perennial succulent belongs to the sedum or stonecrop family. This moderate grower attains a height of about 6 inches and a spread of 18 to 20 inches. The foliage of Blue Pearl is a distinctive, deep purplish blue. Several non patented varieties are deep, bluish green. Late in summer, Blue Pearl produces beautiful clusters of

Blue Pearl is a Sedum hybrid developed by Chris Hansen, a plant breeder fascinated with these ultra-hardy perennials. This perennial succulent belongs to the sedum or stonecrop family. This moderate grower attains a height of about 6″ inches and a spread of 18″ to 20″ inches.

The foliage of Blue Pearl is a distinctive, deep purplish-blue. Several non-patented varieties are deep, bluish-green. Late in summer, Blue Pearl produces beautiful clusters of showy pink flowers atop strong stems. The fragrant blossoms are attractive to hummingbirds, butterflies, and other pollinators. Because they are late-blooming, they provide an important source of pre-winter nourishment for these creatures.

Intense Blue Pearl makes this unique new selection a great choice for landscape or patio. Remains colorful all summer long, unlike other varieties. Large, contrasting hot pink flower clusters dance above the foliage in late summer. Useful as a colorful ground cover or feature in a rock garden or container.


Care Tips

Light: Blue Pearl is more like semi-shade, exposure to sunlight may burn the beads, too little light is not strong growth. When raising them indoors, you can put them on the balcony and ensure that they get more than three hours of light a day, but be careful to avoid direct sunlight when attacking them.

Water: It mainly depends on the dryness of the soil. It is a succulent leaf, juicy and drought tolerant, usually the potting soil is almost dry before watering. It should be watered properly during the week, usually after the soil has dried out, so that the water is not excessive. Also, watering should be reduced in summer and winter because it is in a dormant state.   

Soil: The preferred soil has loose characteristics because the roots of Blue Pearl need a lot of oxygen, which tends to rot in soggy, stuffy soil, and insufficient oxygen can breed a lot of anaerobic bacteria. The soil chosen should also have good drainage and at the same time have some water holding capacity.

Potting: It is recommended to use ceramic pots, ceramic pots have a certain degree of permeability, clay pots lose water too fast, plastic pots retain water too strong, and poor permeability.  

Temperature: The growing temperature is 59-77°F (15-25℃), with spring and summer being the peak growing seasons. Winter temperatures should not fall below 41°F (5℃), which can easily frostbite the leaves and tubers and cause the plant to die. If possible, place the plant outside in a ventilated place and move it indoors in winter.  

Humidity: Blue Pearl grows well in average household humidity levels when grown indoors. Does not like too much humidity. Normal household humidity is good for this plant.

 

Shipping & Handling

    • The 2 Inch Sedum 'Blue Pearl' plants are shipped with the pot and soil
    • The 4 Inch and larger plants are shipped bare roots without the pot and soil:
    • You will receive a very similar plant to the one shown in the photos; shape and color may vary
    • Ship within USA & its outlying territories only
    • Please visit Order Processing & Shipping info page for additional details

     

    Care Instructions

    Please visit our Succulent Care info page for more details.

    To ensure the health of succulents, it is important to plant them in porous, well-draining soil. Succulents require little watering, but don't like to sit in wet soil. To create an adequate cactus mix, simply add pumice, perlite, or grit to cactus soil to provide the proper drainage.

    Make sure to leave drought periods between waterings to prevent the plant from water-logging.

     

    Weather Conditions

    • When ordering, be mindful that living succulents can be damaged by the cold weather.
    • If you live in an area that is below 40 degrees Fahrenheit, please add a shipping warmer to your order or consider purchasing plant until the weather is more suitable.
    • Shipping Warmer: 72+ Hours Heat Packs available for $1.7 each
      Shipping Notes
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      Exchange/Return Notes
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      SKU: 10628511125

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      4.6 ★★★★★
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      Richard Hackathorn
      Grantham, 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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      Amazon Customer
      Houston, 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
      Lexington, 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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      Tommy Jonsson
      Belleville, US
      ★★★★★ 5
      Cover many areas in detail and recommendations for more to read for what's outside
      Format: Paperback
      Good book!
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      Reviewed in the United States on May 4, 2026
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      Moses Kayanda
      Los Angeles, US
      ★★★★★ 5
      One of the best machine learning books...
      Format: Paperback, Format: Paperback
      Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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      Reviewed in the United States on March 1, 2022

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