/plant-recommender

An item-based plant recommender system utilizing spectral clustering to recommend plant species using growing conditions.

Primary LanguageJupyter NotebookThe UnlicenseUnlicense

Plant Recommender Project

Contents

Background

Recently (particularly after 2020), gardening and horticulture have exploded in popularity. This can be seen in news articles such this article from Agweek talking about record sales of seeds in 2020 and other trends. This indicates that there are many more beginner gardeners on the internet than ever before.

There also happens to be a lot of horticultural information available online. Simply look up a common species of houseplant and your search results will be populated with page after page of care guides and nurseries that offer said plant. However, it can be difficult to even know what plants to look up in the first place - rightfully so, because the question of "what plant should I grow" is harder to answer than it first seems.

This is why I decided to start this project - I hope I'll be able to make answering this question a bit easier.

Problem Statement

The objective of this project was to create a recommender system that can take in user inputs about growing conditions and output recommended plant species using a spectral clustering method.

Repository Directory

Here's a list of all the objects contained in this repository and links to each object:

Description of Data

Data was gathered from the USDA PLANTS Database, which contains information about tens of thousands of plant species found across North America and other US territories. Unfortunately, the API used to acess this dataset was offline at the time of building this model, so I instead sourced my data from a dump of the database on sckott's usdaplants API.

Once the data was pulled into a pandas dataframe, I was able to filter out null values (represented as empty strings) and one-hot encode the many categorical features present in this dataset. After doing this, I was left with the following dataset:

Dataset Size Dimensions Source
cleaned-data.csv 650 KB 1078 rows x 279 columns PLANTS Database

This dataset had the following features:

Feature Type Description
id int Identification number used in the PLANTS database
Scientific_Name_x object The scientific name for a plant
Category object A broad classification for the plant (i.e. angiosperm, gymnosperm)
Family object The taxonomical family of a plant
Growth_Habit object A decription of how a plant grows
Native_Status object A description of the regions where a plant is found and whether or not said plant is native to the region
Active_Growth_Period object A description of the time of year a plant is actively growing
Fall_Conspicuous object Whether or not a plant has showy fall foliage
Fire_Resistance object Whether or not a plant is resistant to wildfires
Flower_Color object The primary color of a plant's flowers
Flower_Conspicuous object Whether or not a plant has noticeable flowers
Fruit_Conspicuous object Whether or not a plant has ornamental or edible fruit
Growth_Rate object A plant's growth speed
Lifespan object A plant's average lifespan
Toxicity object The toxicity of a plant to humans
Drought_Tolerance object The drought tolerance of a plant
Hedge_Tolerance object A plant's tolerance of being pruned into a hedge
Moisture_Use object The water use for a given plant
pH_Minimum float The minimum soil pH a plant will tolerate
pH_Maximum float The maximum soil pH a plant will tolerate
Salinity_Tolerance object A plant's tolerance of salty conditions
Shade_Tolerance object A plant's tolerance of shady conditions
Temperature_Minimum_F float The minimum temperature (F) a plant can survive
Bloom_Period object The time of year a plant blooms

After cleaning the data, I also performed some EDA to get a better sense of what the data looked like.

Histogram of plant orders Bar chart for plant growth habits The two charts above show that there is a decent amount of variety in the dataset, although there are a lot of graminoid grass species present.

Modeling

The overall modeling process for this project is as follows: Model Flowchart

Clustering

To actually produce suggestions, there needs to be a way to sort the data, which is where clustering models come in handy. I started with a DBSCAN model to find the number of clusters that worked best for the dataset (which turned out to be 68) and tried out KMeans and SpectralClustering models as well to find the model with the best Silhouette score.

Model Number of Clusters Silhouette Score
DBSCAN 68 0.14
KMeans 70 0.05
Spectral Clustering 5 0.51

Details regarding the other iterations of each clustering model can be found in this notebook. The order of the models above was the same order that I created each model in.

The best clustering model by far turned out to be the SpectralClustering model, after it was tuned (as you can see above). The specific hyperparameters for this model were:

Gamma Number of Clusters n_init
0.5 5 5

Creating the Recommender System

After creating the SpectralClustering model, the actual recommender system could be created. The system I created is an item-based recommender, which uses the similarity between items (in this case, plants) to make recommendations. I created this model twice: once in the form of a function in this notebook and a more refined version in a Streamlit app. The pure Python function was mainly intended as a very bare-bones test case, and the Streamlit app was meant to be a final user-facing form to interact with the model.

If you'd like to run the app, it's hosted here on Streamlit's cloud services

In case you can't run the online version of this app, there's an offline version available as well. The offline version of the Streamlit app I created is here. If you'd like to run this app locally, make sure the Streamlit library is installed on your local machine:

conda install streamlit

And then run the code from your local copy of the repository as follows:

streamlit run plant_recommender_local.py

Findings and Recommendations

Overall, I would consider this project a moderate success. The Streamlit app works as intended and makes decent predictions. However, I do notice three issues with the app:

  1. The app runs slowly - unfortunately, the entire dataset has to be re-clustered each time a query is made so each query can take a few minutes to output suggestions.
  2. The clusters made via Spectral Clustering are large - this can lead to situations where the clusters are too large to allow the model to make precise suggestions. I noticed some results had plant species that didn't make a lot of sense given the query.
  3. There are a lot of grasses in this dataset. This can be seen in the plant order histogram above (lots of Cyperales). This means that with any given query, there is a good chance a grass will be included, which isn't helpful for most beginner gardeners, but could prove useful for things like habitat restoration.

With these results in mind, I have a few ideas for improvements and places to go with this project for the future:

  • Add links to online resources and images of each plant suggested to make the app more user friendly.
  • Obtain more data on more commercially-available species and cultivars. This dataset was full of native species, some of which are not grown by any nursery.
  • Add more features from the PLANTS database to see if this improves the performance of the model
  • Add a way for users to specify the region they reside in. This would allow users to filter through results and select only native species (or filter out invasive species)

Additional Citations