Frequency encoding - should I make dummy columns?

I am busy prepping data to train a machine learning model. Many of the features can work well with one-hot encoding. For one feature, the frequency is related to the target variable. I have been reading up a bit about frequency encoding and everything I find has to do with replacing the category with the frequency, like so:

CategoryName
------------
Blue
Blue
Blue
Yellow
Yellow
Red

Turning into:

CategoryName
------------
3
3
3
2
2
1

If I had done one-hot, I would have had:

Blue   Yellow   Red
1      0        0
1      0        0
1      0        0
0      1        0
0      1        0
0      0        0

All of the above may be related to a single line in the data I want to join it to, so I was wondering if this would be useful:

Blue   Yellow   Red
3      2        1

If so, is there a simple way to do it, like get_dummies()?

With [tag:pandas] you can use a self groupby.transform('size'):

out = df.groupby('CategoryName')['CategoryName'].transform('size')

If you already calculated the dummies, just map their sum:

dummies = pd.get_dummies(df['CategoryName'])
out = df['CategoryName'].map(dummies.sum())

Output:

0    3
1    3
2    3
3    2
4    2
5    1
Name: CategoryName, dtype: int64

You can use value_counts as mapping dict:

>>> df['CategoryName'].replace(df.value_counts())
0    3
1    3
2    3
3    2
4    2
5    1
Name: CategoryName, dtype: int64

Note: take care if some values have the same frequency like ['Blue', 'Blue', 'Yellow', 'Yellow', 'Red'], the result will be [2, 2, 2, 2, 1]

Update

To get only one line:

>>> df.value_counts().to_frame().T
  Blue Yellow Red
0    3      2   1

Update 2

> The target variable can be influenced by how many applications the orchard has had, not just whether it had an application or not.

It makes sense so in this case, all fertilizers should be process as separated variables:

>>> df
  Orchad Fertilizer
0      A         F1
1      A         F1
2      A         F1
3      A         F1
4      A         F2
5      A         F2
6      B         F1
7      B         F1
8      B         F1
9      B         F3

>>> pd.crosstab(df['Orchad'], df['Fertilizer'])
Fertilizer  F1  F2  F3
Orchad                
A            4   2   0
B            3   0   1

# Use normalize={'all'|'index'|'columns'} or StandardScaler
>>> pd.crosstab(df['Orchad'], df['Fertilizer'], normalize='columns')
Fertilizer        F1   F2   F3
Orchad                        
A           0.571429  1.0  0.0
B           0.428571  0.0  1.0