SKU: 47306957889
dracaena interior

dracaena interior Dragon plant - Dracaena Fragrans

Sale price$26.93 Regular price$29.92
Save 10%

Pay in installments of $7.48 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Jul 31 - Aug 5

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

dracaena interior Dragon plant - Dracaena FragransThe Dracaena or Dragon Tree is a bold statement plant with a rugged jungle look. Known as much for the dark and woody trunk as the foliage, Dracaena are some of the most unusual houseplants you can grow. The Dracaena at Happy Houseplants usually have several trunks topped with a crown of beautiful leaves in gold, green or yellow with flashes of pink and red. Dracaena complement any interior style and they are very easy to look after. As the plant

The Dracaena or Dragon Tree is a bold statement plant with a rugged jungle look. Known as much for the dark and woody trunk as the foliage, Dracaena are some of the most unusual houseplants you can grow. 

The Dracaena at Happy Houseplants usually have several trunks topped with a crown of beautiful leaves in gold, green or yellow with flashes of pink and red. 

Dracaena complement any interior style and they are very easy to look after. As the plant stores water in its trunk, it can cope with some neglect if you forget to water it for a few weeks. 

At happy houseplants our tip for Dracaena is to go as large as possible, these are very impressive plants when allowed the space and time to mature. 

Quick Guide 

Light: High or Direct Light
Watering: Only water when soil is dry to the touch
Pets: This plant is toxic to cats and dogs

Care Guide: Ideal For Beginners

Ideal Location: Dry sunny spot

Size: W21cm x H115cm

Houseplants Care

Although the care of houseplants can vary from species to species because they all come from different environments around the world there are a few basic rules to follow that will ensure you have the best chance of success keeping your plant healthy and well in your home. 

Light

Light is critical for any plant, most houseplants will thrive in indirect light throughout the whole day. In general, plants that have variegated leaves or flowers will require more light than other plants. Cactus or succulents are typically the only plants that can tolerate direct light in the summer months. Move plants away from direct light in the summer to avoid burned leaves. 

Temperature

Most houseplants you buy will thrive between 60-72 Fahrenheit, if your plant becomes too cold or too hot it will show signs of distress such as dropped leaves or wilting. Most modern homes will stay between these temperatures but if you go away remember to move your plants to a warm spot in winter or away from direct sunlight in summer. 

Watering

Watering little and often through the growing season is ideal with less watering in the winter months when most plants become dormant and stop growing. The frequency of watering will vary depending on the size of your plant, the size of your planter, the location of your plant and the type of soil you use. Typically we recommend checking the soil carefully before you water, to ensure the soil has not become waterlogged, the soil should dry out completely between each watering. Overwatering is the number one killer of houseplants, if you overwater and your plant is dying, repot immediately.  

Humidity

Almost all the houseplants you find for sale will naturally grow in the warm and humid tropics. Most modern centrally heated homes are dry in winter so almost all plants will benefit from regular misting or being placed on a tray of pebbles with a small amount of water that will naturally evaporate into the air. Plants also can also benefit from being placed and grown together to create a natural micro climate. 

Feeding

Feeding your plants through the growing season can have real impact. Using a good quality feed like Happy Houseplants own vegan plant food can boost your plants immune system and help it grow quickly. 

When to Repot?

Most plants will be very happy for 1 to 2 years in the pot they arrive in but depending on the growth of your plant, when it does require repotting a planter 2/3 inches bigger is usually enough. Most plants will respond well to repotting growing well after the roots have been disturbed allowing more room and oxygen into the soil. Use a general purpose potting soil (John Innes number 3) and ensure any planter you use has sufficient drainage which is critical. 

Problems

All plants take time to recover being moved from grower to seller to their new home, some plants will look sad for a few weeks but this is normal and they will recover when they have adapted to their new home. Sometimes plants need a bit more care, be confident changing your routine or moving a plant to see if it will grow better in a new spot. 

Long Stems or 'leggy' plants usually means your light levels are too low. 

Brown & Black Leaves usually means too much light or feeding is excessive

Leaf Drop Some leaf drop is normal and to be expected, most plants will want to grow taller and will drop lower leaves naturally as they grow taller.  Excessive leaf drop usually means you have overwatered. Repot with dry soil immediately and your plant may recover. 

Wilting and Drooping Leaves means you have usually been under watering. Almost all plants will recover quickly if you soak and drain the soil. 

Faded Variegation usually means the plant is not getting enough indirect light. try moving your plant to a different spot. 

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 47306957889

Discover Niche Categories That Outsell dracaena interior

Top-Converting Item to Boost Your Average Order

4.6 ★★★★★
Based on 21 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
C
Verified Purchase
Carol
Grantham, US
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 24, 2019
W
Walter Echo-Hawk, author of THE SEA OF GRASS.
Draper, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
P
Verified Purchase
Par
Louisville, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Charlottesville, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Fort Morgan, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025

recommand products