SKU: 64455651849
small planter indoor

small planter indoor Small Aquarium Wicking Pot – Compact Fish Tank Planter

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Description

small planter indoor Small Aquarium Wicking Pot – Compact Fish Tank PlanterSmall Aquarium Mounted Wicking Pot Compact Self Watering Planter Product Specifications Fits rimmed and rimless aquariums Compact planting chamber Designed for continuous moisture environments Compatible with Mossari extensions Ships in 12 business days Add tropical greenery to your aquarium without taking over the tank. The Mossari Small Aquarium Mounted Wicking Pot is a compact self watering planter designed to fit both rimmed and rimless aquariums.

Small Aquarium-Mounted Wicking Pot – Compact Self-Watering Planter

Product Specifications

• Fits rimmed and rimless aquariums
• Compact planting chamber
• Designed for continuous moisture environments
• Compatible with Mossari extensions
• Ships in 1–2 business days

Add tropical greenery to your aquarium without taking over the tank.

The Mossari Small Aquarium-Mounted Wicking Pot is a compact self-watering planter designed to fit both rimmed and rimless aquariums. Built from aquarium-safe materials, it mounts securely to your tank and uses passive capillary action to draw water upward into the root zone.

No pumps. No plumbing. No daily watering.

This smaller format is ideal for nano tanks, tighter setups, or hobbyists who want to experiment with above-tank plant growth without committing to a larger planter.

How the Aquarium Wicking System Works

A wick extends from the planter into your aquarium water. As the plant uses moisture, water is continuously drawn upward and distributed evenly throughout the substrate.

This creates a stable, consistently moist root zone that supports steady growth without the harsh wet-dry cycle of traditional watering.

The Small Wicking Pot works especially well for compact tropical plants that prefer consistent moisture, including small trailing vines, creeping species, ferns, and lightweight foliage plants.

Substrate Options – Choose Your Growing Strategy

Customize your planter with one of two wicking substrate blends depending on how you want your aquarium system to perform.

Enriched Wicking Substrate – Added Nutrient Support

Designed for growers who want stronger establishment and added nutrient buffering in the root zone.

Includes:
• Coco coir, perlite
• Worm castings
• Kelp meal
• Neem meal
• Gypsum

Why upgrade to Enriched?

Worm castings provide gentle plant-available nutrients and beneficial microbial activity that support root health.

Kelp meal supplies trace minerals and natural growth compounds that encourage fuller foliage and stronger development.

Neem meal contributes organic matter and supports a balanced root-zone environment.

Gypsum provides calcium for structural plant support and helps maintain substrate structure.

This option is particularly helpful for lightly stocked aquariums, new tanks, or smaller systems where nutrient levels may be lower.

When used properly in a Mossari wicking system, amendments remain primarily concentrated in the root zone rather than freely washing into the aquarium.

Inert Wicking Substrate – Full Feeding Control

Designed for fully aquarium-driven nutrient systems.

Includes:
• Coco coir
• Coco chips
• Perlite

No added fertilizers or amendments.

Plants rely entirely on nutrients drawn from aquarium water. Ideal for established tanks with steady nitrate production or growers who prefer complete nutrient control.

How Much Does 6 Cups Fill?

One 6-cup bag fills:

Designed for Plant and Fish Health

The wicking system separates the plant root zone from direct water flow. Water moves upward through capillary action rather than pouring through the substrate.

This helps:

• Maintain stable root moisture
• Avoid heavy nutrient dumping into the aquarium
• Allow plants to assist in nutrient uptake from tank water

Why Choose Mossari

Mossari planters are engineered specifically for aquarium integration. They are not modified pots or generic clip-on containers. Each design is built around wicking performance, structural stability, and long-term durability in humid environments.

The Small Wicking Pot gives you flexibility to test new plants, expand vertically, or add greenery without overwhelming your aquarium footprint.

Create a Living System

Add compact tropical plants above your tank. Reduce manual watering. Turn unused vertical space into a functional plant-aquarium display.

Shipping Notes
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Exchange/Return Notes
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SKU: 64455651849

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Hashi Hanta
Birmingham, US
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
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Carol
Fort Morgan, 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.
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Reviewed in the United States on November 24, 2019
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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.
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Reviewed in the United States on April 1, 2019
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Par
Belleville, 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
Carnegie, 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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