SKU: 90894673329
snake plant tall skinny leaves

snake plant tall skinny leaves Laurentii

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Description

snake plant tall skinny leaves LaurentiiDracaena (Sansevieria) trifasciata 'Laurentii' Dracaena trifasciata 'Laurentii' is the familiar yellow edged snake plant, with tall sword shaped leaves rising in firm fans from the base. Each leaf has a deep green centre marked with softer grey green horizontal banding, framed by clear yellow margins that make the leaf shape stand out even from a distance. The result is strong, recognisable and clear in a pot. This cultivar brings height in a slim

Dracaena (Sansevieria) trifasciata 'Laurentii'

Dracaena trifasciata 'Laurentii' is the familiar yellow-edged snake plant, with tall sword-shaped leaves rising in firm fans from the base. Each leaf has a deep green centre marked with softer grey-green horizontal banding, framed by clear yellow margins that make the leaf shape stand out even from a distance. The result is strong, recognisable and clear in a pot.

This cultivar brings height in a slim space. It grows from a rhizome, so new leaves appear as basal shoots beside the older fans. Over time, a young plant becomes a fuller clump as fresh leaves push up from the base and add more layers to the yellow-edged outline.

Classic yellow margins on tall sword leaves

  • Leaf shape: Tall, sword-like blades create a strong vertical line.
  • Colour contrast: Yellow margins frame the banded green centre of each leaf.
  • Growth base: The rhizome sends up new leaves beside older fans, gradually thickening the clump.
  • Indoor placement: It gives height while taking up limited floor or shelf space.
  • Longevity: Mature leaves stay firm for a long time when the root zone is kept warm, airy and dry between waterings.

Rhizome storage and dry intervals

Dracaena trifasciata is a rhizomatous species from seasonally dry tropical parts of Africa. 'Laurentii' stores water in thick leaves and depends on oxygen around the rhizome after watering. The plant handles dry intervals well because the leaves and underground structure are built for moisture storage.

'Laurentii' keeps the strong leaf form of the species and adds bright marginal colouring. The yellow edges are part of the cultivar’s visual identity, while the grey-green striping across the blade gives the centre more depth. Mature leaves can become tall and rigid, so the pot should be stable enough to balance the top growth.

Growth is usually slow indoors, especially in winter or away from bright windows. New shoots may appear narrow at first before expanding into stronger leaves. A slightly snug pot keeps the rhizome stable and lets the substrate dry at a predictable pace.

Care for tall yellow-edged leaves

  • Light: In bright indirect light, new leaves stay sturdier and the contrast remains clearer. In lower light, the plant grows more slowly and the pot needs longer drying time.
  • Watering: Wait until the potting mix has dried deeply, then water evenly and let the pot drain fully. The next watering should come after the lower mix has dried again.
  • Substrate: Use a free-draining mix with pumice, lava rock, coarse sand or fine bark. Mineral structure keeps air around the rhizome.
  • Pot choice: A pot with drainage holes and enough weight for tall leaves keeps the plant steady.
  • Temperature: Keep it in normal indoor warmth, ideally around 18–27 °C. Warm conditions help the root zone recover after watering.
  • Humidity: Average household humidity is sufficient.
  • Feeding: Use a diluted balanced or cactus fertiliser during active growth. Light feeding matches the plant’s slow rhizome growth.
  • Repotting: Repot when new shoots crowd the pot, the container starts to distort or the substrate has broken down. A modest size increase is enough.
  • Propagation: Division preserves the yellow-edged pattern. Leaf cuttings can root and may produce green plants.

Yellow-edge stress signs

  • Soft leaf bases: Inspect the substrate depth, cover pot and rhizome area. Softness near the soil line usually means the lower plant stayed damp for too long.
  • Wrinkled leaves: Check the root system before adding more water. Dryness and damaged roots can both produce a wrinkled leaf surface.
  • Brown margins: Review irregular watering, mineral buildup, old knocks and cold air exposure. Remove only the dry edge if trimming is needed.
  • Leaning leaves: Rotate the pot and check whether new shoots are pressing older leaves sideways. Mature plants may need a heavier pot for a steady base.
  • Paused growth: Growth often slows in winter. Check light and warmth first, then adjust feeding during active growth if needed.

Safety for shared spaces

Keep Dracaena trifasciata 'Laurentii' out of reach of pets and small children who may chew the leaves. Snake plants contain saponins, which can cause nausea, vomiting or diarrhoea in cats and dogs if ingested. The tall, firm leaves also need a secure spot where the pot stays steady.

Botanical name of the classic snake plant

The accepted botanical name for the species is Dracaena trifasciata, while Sansevieria trifasciata remains the older name still widely used in plant shops and care guides. The genus name Dracaena comes from the Greek drakaina, meaning “female dragon”, a name historically linked to red resin in some dragon tree relatives. The species epithet trifasciata means “three-banded” or “marked with three bands”, referring to the banded pattern associated with the species.

Dracaena trifasciata 'Laurentii' has tall green leaves, yellow margins and one of the most recognisable snake plant forms.

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★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
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Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
San Leandro, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Omaha, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
Boise, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018
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Stergios Papadimitriou
Fort Morgan, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on August 25, 2018

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