The PlantBase team created an AI assistant to help plant owners with plant care. It can predict the type of flower from an uploaded image with 61% accuracy for the top result and 86% for the top 3 results. It provides individualized care instructions by incorporating weather forecasts. To improve the model, the team sourced data on 16 common UK flowers, addressed data issues, used transfer learning from VGG-16, and techniques like data augmentation and class weighting to address overfitting and class imbalance. The goal is to help non-experts better care for their plants.

1. Wagon #475

2. PlantBase team
Anna Kondratiuk Roxane MomeniJoshua Eagle Michael Hayman

3. PlantBase: your personal gardening assistant
What problem does it solve?
Many plant owners today are not
familiar with the care their plants
actually need and end up
improvising a care plan
Predict the flower group
Upload an image of your flower and receive an
instant prediction (16 groups so far)
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Suggest individualised plant care
Receive detailed gardening information for your
plant with integrated 5-day weather forecast for
quick and up-to-date advice
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Currently, PlantBase has an accuracy of 61% for
top 1 plant prediction and 86% for top 3 plants

4. Sourced the data and determined categories of flowers to focus on
Developed baseline convolutional neural network model
Addressed a number of issues with regards to image pre-processing
Leveraged pre-trained VGG-16 model
Our approach
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5. We selected 16 popular UK plant
genera as a subset
● Training set: 4,400 images
● Test set: 1,400 images
.. and incorporated:
Royal Horticultural Society
plant care advice
Metaweather forecast
Examples:
Our model relies on a premade PlantCLEF image
classification dataset
● Includes 1k plant species from Western Europe
● Composed of ~100k images uploaded by users to
Pl@ntNET
● Used previously for an plant image classification
competition
Sourcing the data & determining a focus group
Daffodil Geranium
Source: ImageClef / Plantnet, Royal Horticultural Society, Metaweather API

6. Heavily skewed by the
sample size for each
genus
→ 3 convolutional and pooling layers, a flattening and a
dense output layer to predict 16 different categories
Initial deep-learning model..
Our baseline CNN model
… only yielded 30% accuracy
Doesn’t recognise
flower genuses with
lower number of
images

7. We used ImageDataGenerator from
TensorFlow to transform an image every
time the model ran to generalise more easily
solved by..
Our model was predicting the majority
class overly frequently and not
predicting some of the minority classes
Our model was very effective at
predicting images in our training set but
did not generalise well to our test data
Overfitting issue
Pre-processing our data to improve our model
Class imbalance issue
Data augmentation
We instructed the model to assign more
value to the minority classes, encouraging
it to treat each class more equally
Class weighting

8. 86% accuracy for top 3
plants prediction!
Transfer learning from VGG-16 model
We adjusted the VGG-16 model to match our
classification
● VGG-16 is a CNN model using 16 layers, pre-trained on over 14
million images (Simonyan & Zisserman, Oxford)
Accuracy: 61%

9. Product demo